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   "source": [
    "#format the book\n",
    "%matplotlib inline\n",
    "from __future__ import division\n",
    "import matplotlib.pyplot as plt\n",
    "import book_format\n",
    "book_format.load_style(name='/custom3.css')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Particle Filters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If the system or measurement models are nonlinear and the noise is non-Gaussion, how do we handle the problem?\n",
    "    * **Extended Kalman Filter** applies an linearization approximation to the nonlinear function rather than to the probability distribution. It is sensitive to strong nonlinearities and non-Gaussian noise, and is only reliable for systems which are almost linear on the time scale of the update intervals.\n",
    "    * **Unscented Kalman Filter** gives a better approach that use the exact nonlinear function applied to an approximating probability distribution. It still uses a Gaussian approximation, which is not always satisfactory \n",
    "* **Particle filters** or **Sequential Monte Carlo** (SMC) methods are a set of on-line posterior density estimation algorithms that estimate the posterior density of the state-space by directly implementing the Bayesian recursion equations.\n",
    "* **Particle filters** use a sampling approach, with a set of particles to represent the posterior density.\n",
    "    * Each particle has a weight assigned to it that represents the probability of that particle being sampled from the probability density function.\n",
    "    * Particles often scale exponentially. It would be a mistake to represent particle filters over anything more than 4 dimensions.\n",
    "    * The key advantage of particle filters is that they are the easiest to program and the most flexible filters.\n",
    "    \n",
    "    \n",
    "    \n",
    "* **Generic Particle Filter Algorithm**\n",
    "\n",
    "    1. **Randomly generate a bunch of particles**\n",
    "    \n",
    "      Particles can have position, heading, and/or whatever other state variable you need to estimate.\n",
    "  Each has a weight indicating how likely it matches the actual state of the system.\n",
    "  \n",
    "    2. **Predict next state of the particles**\n",
    "\n",
    "      Advance the particles to the next time step based on a system model and noise model.\n",
    "  \n",
    "    3. **Update**\n",
    "\n",
    "      Update the weighting of the particles based on a measurement.\n",
    "  \n",
    "    4. **Resample**\n",
    "\n",
    "      Discard highly improbable particles and replace them with copies of more probable particles\n",
    "  \n",
    "      Optionally, compute mean and covariance of the set of particles to get the most likely current state.\n",
    "  \n",
    "  \n",
    "  \n",
    "  \n",
    "This naive algorithm runs into some practical difficulties which we will need to overcome, but this is the general idea. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Robot Location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a floor plan of an environment where a robot is located and the robot has to perform global localization. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Robot Location](floor.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The robot, which is located in the upper right hand corner of the environment, has range sensors that are represented by the blue stripes.\n",
    "    * The sensors use sonar sensors to range the distance of nearby obstacles to determine a good posterior distribution as to where it is.\n",
    "    * What the robot doesn't know is that it is starting in the middle of a corridor and completely uncertain as to where it is.\n",
    "* The red dots are particles.\n",
    "    * They are a discrete guess as to where the robot might be.\n",
    "    * These particles are structured as an x coordinate, a y coordinate and also a heading direction — three values to comprise a single guess.\n",
    "    * However, a single guess is not a filter, but rather it is the set of several thousands of guesses that together generate an approximate representation for the posterior of the robot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The essence of particle filters here:\n",
    "    * To have the particles guess where the robot might be moving\n",
    "    * To have them survive, a kind of \"survival of the fittest,\" so that particles that are more consistent with the measurements, are more likely to survive.\n",
    "        * As a result, places of high probability will collect more particles, and therefore be more representative of the robot's posterior belief.\n",
    "    * The particles together, make up the approximate belief of the robot as it localizes itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simpler Robot World"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A robot lives in a world of 100x100, if it moves to one end, then it appears on the other — it is a cyclic world.\n",
    "* It can turn, move straight after the turn, and it can sense the distance to four designated landmarks ([20.0, 20.0], [80.0, 80.0], [20.0, 80.0], [80.0, 20.0])."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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kDbgAho2/oQBEhPmT56vpp0361rhvae5lc5Wfmq/qI9X6ffXvQz00RJj+QhsAgo3YBhAx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\nMAmxDQAAAJiE2AYAAABM8v8BTpPPSpsppksAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xafd063ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "landmark_bg = '#CCCCCC'\n",
    "robot_bg = '#88CCFF'\n",
    "arrow_bg = '#88FF88'\n",
    "\n",
    "#plt.figure(figsize=(8,8), facecolor='w')\n",
    "#ax = plt.axes((0, 0, 8, 8), xticks=[], yticks=[], frameon=False)\n",
    "#ax.set_xlim(0, 10)\n",
    "#ax.set_ylim(0, 10)\n",
    "#plt.xlim([0,100]);\n",
    "#plt.ylim([0,100])\n",
    "ax = plt.axes()\n",
    "#ax = plt.axes((0, 0, 40, 40), xticks=[], yticks=[], frameon=False)\n",
    "\n",
    "ax.add_patch(Rectangle((0, 0), 100, 100, edgecolor=\"red\", facecolor=\"orange\", fill=False))\n",
    "\n",
    "plt.scatter ([20,20,80,80], [20,80,20,80], s=512, color=landmark_bg)\n",
    "plt.scatter ([40], [40], s=1024, color=robot_bg)\n",
    "\n",
    "ax.annotate('', xy=(20,80), xytext=(40,40),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(20,20), xytext=(40,40),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(80,80), xytext=(40,40),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(80,20), xytext=(40,40),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(50,45), xytext=(40,40),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec=robot_bg,\n",
    "                            #shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.axis([-10,110,-10,110])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Robot Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code will allow us to make a robot move along the x and y coordinates as well as in the heading direction. Take a minute to familiarize yourself with this code and then see how you can use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import * \n",
    "import random\n",
    "\n",
    "landmarks  = [20.0, 20.0], [80.0, 80.0], [20.0, 80.0], [80.0, 20.0]\n",
    "world_size = 100.0\n",
    "\n",
    "class robot:\n",
    "    def __init__(self):\n",
    "        self.x = random.random() * world_size \n",
    "        self.y = random.random() * world_size \n",
    "        self.orientation = random.random() * 2.0 * pi \n",
    "        self.forward_noise = 0.0; \n",
    "        self.turn_noise    = 0.0; \n",
    "        self.sense_noise   = 0.0;\n",
    "\n",
    "    def set(self, new_x, new_y, new_orientation):\n",
    "        if new_x < 0 or new_x >= world_size:\n",
    "            raise ValueError, 'X coordinate out of bound'\n",
    "        if new_y < 0 or new_y >= world_size:\n",
    "            raise ValueError, 'Y coordinate out of bound'\n",
    "        if new_orientation < 0 or new_orientation >= 2 * pi:\n",
    "            raise ValueError, 'Orientation must be in [0..2pi]'\n",
    "        self.x = float(new_x) \n",
    "        self.y = float(new_y) \n",
    "        self.orientation = float(new_orientation)\n",
    "\n",
    "    def set_noise(self, new_f_noise, new_t_noise, new_s_noise):\n",
    "        # makes it possible to change the noise parameters \n",
    "        # this is often useful in particle filters \n",
    "        self.forward_noise = float(new_f_noise);                     \n",
    "        self.turn_noise    = float(new_t_noise); \n",
    "        self.sense_noise   = float(new_s_noise);\n",
    "\n",
    "    def sense(self):\n",
    "        Z = [] \n",
    "        for i in range(len(landmarks)):\n",
    "            dist = sqrt((self.x - landmarks[i][0]) ** 2 + (self.y - landmarks[i][1]) ** 2) \n",
    "            dist += random.gauss(0.0, self.sense_noise) \n",
    "            Z.append(dist)\n",
    "        return Z\n",
    "\n",
    "    def move(self, turn, forward):\n",
    "        if forward < 0:\n",
    "            raise ValueError, 'Robot cant move backwards'\n",
    "        # turn, and add randomness to the turning command \n",
    "        orientation = self.orientation + float(turn) + random.gauss(0.0, self.turn_noise) \n",
    "        orientation %= 2 * pi \n",
    "\n",
    "        # move, and add randomness to the motion command \n",
    "        dist = float(forward) + random.gauss(0.0, self.forward_noise) \n",
    "        x = self.x + (cos(orientation) * dist) \n",
    "        y = self.y + (sin(orientation) * dist) \n",
    "        x %= world_size    # cyclic truncate \n",
    "        y %= world_size # set particle \n",
    "        res = robot()\n",
    "        res.set(x, y, orientation) \n",
    "        res.set_noise(self.forward_noise, self.turn_noise, self.sense_noise)\n",
    "        return res\n",
    "\n",
    "    def Gaussian(self, mu, sigma, x):\n",
    "        # calculates the probability of x for 1-dim Gaussian with mean mu and var. sigma \n",
    "        return exp(- ((mu - x) ** 2) / (sigma ** 2) / 2.0) / sqrt(2.0 * pi * (sigma ** 2))\n",
    "\n",
    "    def measurement_prob(self, measurement):\n",
    "        # calculates how likely a measurement should be \n",
    "        prob = 1.0; \n",
    "\n",
    "        for i in range(len(landmarks)):\n",
    "            dist = sqrt((self.x - landmarks[i][0]) ** 2 + (self.y - landmarks[i][1]) ** 2) \n",
    "            prob *= self.Gaussian(dist, self.sense_noise, measurement[i])\n",
    "        return prob\n",
    "\n",
    "    def __repr__(self):\n",
    "        return '[x=%.6s y=%.6s orient=%.6s]' % (str(self.x), str(self.y), str(self.orientation))\n",
    "\n",
    "\n",
    "def eval(r, p):\n",
    "    sum = 0.0; \n",
    "    for i in range(len(p)): # calculate mean error\n",
    "        dx = (p[i].x - r.x + (world_size/2.0)) % world_size - (world_size/2.0) \n",
    "        dy = (p[i].y - r.y + (world_size/2.0)) % world_size - (world_size/2.0) \n",
    "        err = sqrt(dx * dx + dy * dy) \n",
    "        sum += err\n",
    "    return sum / float(len(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a object **robot** and assign it to a variable *myrobot*, and set x=10.0, y=10.0, heading=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[x=10.0 y=10.0 orient=0.0]\n"
     ]
    }
   ],
   "source": [
    "myrobot = robot()\n",
    "myrobot.set(10.0, 10.0, 0.0)\n",
    "print myrobot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, make the robot move 10 meters forward and not turn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[x=10.0 y=10.0 orient=0.0]\n",
      "[x=20.0 y=10.0 orient=0.0]\n"
     ]
    }
   ],
   "source": [
    "myrobot = robot()\n",
    "myrobot.set(10.0, 10.0, 0.0)\n",
    "print myrobot\n",
    "myrobot = myrobot.move(0.0, 10.0)\n",
    "print myrobot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, make the robot turn by $\\pi/2$ and move 10 meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[x=10.0 y=10.0 orient=0.0]\n",
      "[x=20.0 y=10.0 orient=0.0]\n",
      "[x=20.0 y=20.0 orient=1.5707]\n"
     ]
    }
   ],
   "source": [
    "myrobot = robot()\n",
    "myrobot.set(10.0, 10.0, 0.0)\n",
    "print myrobot\n",
    "myrobot = myrobot.move(0.0, 10.0)\n",
    "print myrobot    \n",
    "myrobot = myrobot.move(pi/2, 10.0)\n",
    "print myrobot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then generate measurements with the command **sense** to give the distance to the four landmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[x=10.0 y=10.0 orient=0.0]\n",
      "[x=20.0 y=10.0 orient=0.0]\n",
      "[x=20.0 y=20.0 orient=1.5707]\n",
      "[0.0, 84.8528137423857, 60.0, 60.0]\n"
     ]
    }
   ],
   "source": [
    "myrobot = robot()\n",
    "myrobot.set(10.0, 10.0, 0.0)\n",
    "print myrobot\n",
    "myrobot = myrobot.move(0.0, 10.0)\n",
    "print myrobot    \n",
    "myrobot = myrobot.move(pi/2, 10.0)\n",
    "print myrobot\n",
    "print myrobot.sense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Move Robot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using your interpreter, make a robot that satisfies the following requirements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# starts at 30.0, 50.0, heading north (=pi/2)\n",
    "# turns clockwise by pi/2, moves 15 meters\n",
    "# senses\n",
    "# turns clockwise by pi/2, moves 10 meters\n",
    "# senses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "input_collapsed": true
   },
   "outputs": [],
   "source": [
    "#After printing senses the first time around we get the following output:\n",
    "#\n",
    "#   [39.0, 46.0, 39.0, 46.0]\n",
    "#\n",
    "#After printing sense the second time around we get the following output:\n",
    "#\n",
    "#    [32.0, 53.1, 47.1, 40.3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Add Noise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the noises as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# forward_noise = 5.0, turn_noise = 0.1, sense_noise = 5.0\n",
    "# starts at 30.0, 50.0, heading north (=pi/2)\n",
    "# turns clockwise by pi/2, moves 15 meters\n",
    "# senses\n",
    "# turns clockwise by pi/2, moves 10 meters\n",
    "# senses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Create Particles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Program a particle filter which maintains a set of N (N = 1000) random guesses as to where the robot might be.\n",
    "* Each guess is a vector that contains an x-coordinate, a y-coordinate, and heading direction.\n",
    "    * The heading direction is the angle the robot points relative to the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Fill in the code to assign 1,000 particles to a list p.\n",
    "N = 1000\n",
    "p = []\n",
    "\n",
    "# In order to make a particle set of 1,000 particles, \n",
    "# you have to program a separate piece of code\n",
    "# that assigns 1,000 of those to a list.\n",
    "# Everytime you call the function robot()\n",
    "# and assign it to a particle p[i]\n",
    "\n",
    "print len(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Move Robot Particles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take each particle and simulate robot motion.\n",
    "\n",
    "* Each particle should first turn by 0.1 and then move forward 5.0 meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = 1000\n",
    "p = []\n",
    "for i in range(N):\n",
    "    x = robot()\n",
    "    p.append(x)\n",
    "\n",
    "# make a new set p that is the result of the specific motion,\n",
    "# turn by 0.1 and move forward 5.0 meters,\n",
    "# to all of the particles in p."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Half of Particle Filters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Suppose you have a robot that sits amid four landmarks and can measure the exact distances to the landmarks.\n",
    "* The below image shows the robot's location and the distances it measures, as well as \"measurement noise,\" which is modeled as a Gaussian with a zero mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "input_collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vIhlAvBHKABIesZx+iGQAiYBQBpAUiOX0QSQDSBSEMoCkQSynPiIZQCIhlAEkFWI5dRHJ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\nygAAAEAAhDIAAAAQAKEMAAAABPD/Aby29McxOt8QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xafd7502c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as plt\n",
    "from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow\n",
    "\n",
    "landmark_bg = '#CCCCCC'\n",
    "robot_bg = '#88CCFF'\n",
    "arrow_bg = '#88FF88'\n",
    "\n",
    "#plt.figure(figsize=(8,8), facecolor='w')\n",
    "#ax = plt.axes((0, 0, 8, 8), xticks=[], yticks=[], frameon=False)\n",
    "#ax.set_xlim(0, 10)\n",
    "#ax.set_ylim(0, 10)\n",
    "plt.xlim([0,8]);\n",
    "plt.ylim([0,8])\n",
    "\n",
    "plt.scatter ([1,1,7,7], [1,7,1,7], s=512, color=landmark_bg)\n",
    "plt.scatter ([4], [4], s=1024, color=robot_bg)\n",
    "ax = plt.axes()\n",
    "ax.annotate('', xy=(1,7), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(1,1), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(7,7), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(7,1), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(5,4.5), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec=robot_bg,\n",
    "                            #shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "\n",
    "plt.axis('equal')\n",
    "#plt.axis([0,8,0,8])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Now we have a measurement vector that consists of the four values of the four distances from L1 to L4.\n",
    "* If a particle hypothesizes that its coordinates are somewhere other than where the robot actually is (the red robot indicates the particle hypothesis), we have the situation shown below.\n",
    "* The particle also hypothesizes a different heading direction.\n",
    "* You can then take the measurement vector from our robot and apply it to the particle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "input_collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n1+e9thqSwy6Nz/aevGRwWv/P7YvlsvIyra/wnu76iXn9n+4aAPANQhkATBJs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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xafa63a2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as plt\n",
    "from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow\n",
    "\n",
    "landmark_bg = '#CCCCCC'\n",
    "robot_bg = '#88CCFF'\n",
    "particle_bg = 'r'\n",
    "arrow_bg = '#88FF88'\n",
    "\n",
    "#plt.figure(figsize=(8,8), facecolor='w')\n",
    "#ax = plt.axes((0, 0, 8, 8), xticks=[], yticks=[], frameon=False)\n",
    "#ax.set_xlim(0, 10)\n",
    "#ax.set_ylim(0, 10)\n",
    "plt.xlim([0,8]);\n",
    "plt.ylim([0,8])\n",
    "\n",
    "ax = plt.axes()\n",
    "\n",
    "plt.scatter ([1,1,7,7], [1,7,1,7], s=512, color=landmark_bg)\n",
    "\n",
    "# actual\n",
    "plt.scatter ([4], [4], s=1024, color=robot_bg)\n",
    "ax.annotate('', xy=(5,4.5), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec=robot_bg,\n",
    "                            #shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "# particle\n",
    "plt.scatter ([6], [5], s=1024, color=particle_bg)\n",
    "ax.annotate('', xy=(7,5), xytext=(6,5),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec=particle_bg,\n",
    "                            #shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "\n",
    "ax.annotate('', xy=(1,7), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(1,1), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(7,7), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(7,1), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "\n",
    "plt.axis('equal')\n",
    "#plt.axis([0,8,0,8])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* However, this ends up being a very poor measurement vector for the particle.\n",
    "* The blue indicates the measurement vector we would have predicted if the red particle actually were a good match for the robot's actual location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "input_collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2kl/fKe043vLXL+oune/7tMWnKS727B5v3eoJ48WLjR+18IZQBtAadpQBIMxi\ncWc52HGLnEwpK03qnCZlJku2Zr3fLSPwtTSeR87L85z2OBqRLJ2aUy4uZk4ZwOkIZQAwQCzFcigz\nyUM7Sed19YxgnKjx/G+9K7Ol3gGMSTSfR77hBulf/5IGDPD/GOHGnDKA1jB6AQAGCXYMo7Ra2nVC\n2lMmVdRKdS7JLclqkdLsUrd0Kcfh2eX1NdERbCS73NIXx6SP90tHqzy3ZSZ51lHmlPLaS+d09utQ\nkpqeH1mSfvUrzwVFjDr1WyAuuURascIzfnHbbdFeDYBYQigDgIH8jeU6t7TlsLTliHS0WjpZ2/Ix\nvzourd0vdUiV+jik87pIqV5+mgcTyd4C2ZHsmUPeXy5tOiK1T5Gu6eX/lZ5jZR65JcwpA2gJoQwA\nBvMVy18dk/65zxOmLj+P6XRLByo9H9uPSoM6Sv/R7VS8BhrJrQXy4A7SoSrp7d2eGeUbcqQUm3/r\nfOst6dZbPaMWeXnS8uXRHbXwpvmccs+e0V4RgFgRA7/0AoD4521mubrWrdd3Sit3S4cDiOTmSmuk\nf+2XFn/pidxAItnllrYdlV7cJr2xy/N4R7Jnx/juQdJZnSSb1fNGvj6Z0vV9pC7pvtcUi/PILWFO\nGUBLCGUAiJDmsXxt4X3aftSt6rrQj10naW+FNPk9/yLZ30Cul26Xbs71zCb70vj8yG63Zx75tdci\ne37kQDF+AcAbRi8AIIKu6XeNloxZrpuWFOi9r+fpZK10w+DwnGd5095CFX5+m9xy6cHzvUeyrxEL\nW4jbJ7E+j9wSQhmAN+woA0AEud3SScs1uj1/uezWFK3bPU+vbwn91HGNI/ny3GnK6z5dVY3eEBjo\nDnIwPv44M2bOjxwozqcMwBtCGQAiqOiAtLdcGpAVvlhuHslXDpiuo9XSW7sjE8hut7RgQVdNmpQb\n8/PILWFOGYA3hDIAREi5U9pwSKr9vofDEcveIrne16XS85uNC2Tp1DzyvHk95XZbTDGP3BLGLwA0\nRygDQIR8tNdzhorGQonl1iJZ8pybudxpTCBLnjGFESM8FxHJyKjVM8/s0OOPx8ZFRIJBKANozqQ/\nzgDAXGpdnpELb4KJZV+RXM9u8ZzSLZyBLHnOj9x4Hnnhwu268MLS8H2DKGBOGUBzAf3YfOKJJ2S1\nWnX//fcbtR4AiEv/Piwdq27564HEsr+RLHnGPNYfCHHxjbR0fuTevVv5w5kEc8oAmvM7lNetW6f5\n8+dr6NAUH/YNAAAdkklEQVShYTmNEQAkkm/KJF8DFf7EciCRXK80TA1rxvMjB4rxCwCN+RXKpaWl\nGjdunBYsWKD27f042zwAoIlyp3/3ax7Lb21/uOFr20r+HnAkS9LJWoV8UZPG88iZmdIbb8jU88gt\nIZQBNObXj7gJEyZozJgxuvjii0M+1ycAJJpal3TSz1CWmsbyxr3/1/Bz99/7lwQcyZJU4ZT2VQS4\n6EaazyOb6fzIgWJOGUBjFreP8p0/f77+/Oc/a926dbLZbLr00ks1ZMgQzZ49u8n9SktPvYljx44d\nxqwWAEyoWnb9SwPltCQH9LgjFV/LLbc6ZfSTJJ2sOaYjFTuU3f7cgNcwwP2NeupwQI9xu6WFC7vq\n+ed7yO226OKLj2n69F1q08YV8Pc3k0mT+mnNmnb69a93atSoo9FeDgCD5ebmNnzucDiafK3VS1h/\n+eWXeuyxx7RmzRrZbDZJktvtZlcZAALgllVuBf7ejo4ZfZv8d3pye6UnBx7JkuQK8CRHJ09a9atf\n9dZ773WQJE2YsFc//en+uBu18GbYsDKtWdNOGza0JZSBBNfqjvLChQv1k5/8pCGSJamurk4Wi0U2\nm00VFRVKSkqS1HRHuXmNA2ZTVFQkScrPz4/yShAPyp3Sgm2eWeFAfHfc8/ewZzvP38PSyu+0/8Qm\nDci6NuA3VV99hnR2Z//uW1wsFRR4Ri0yM6XFi32PWsTTc6aoyDNq0q+fxC9IYaR4et6YWWsN2+re\nwI033qgtW7Zo06ZN2rRpkzZu3Kj8/HyNHTtWGzdubIhkAEDL0uxSUoA7sZv2FmrumvO0aP1/NvwW\nb+X2yVq4/vqAr+BnldQh1b/7JtI8ckuYUwZQr9Uf3Q6HQwMHDmz4GDRokNLT09W+fXsNHDgwUmsE\nAFOzWaSMAPYVGp8CbmTv+xt2j4efcXdQV/DLSJK6prd+n5bOjzxggP/rjhecTxlAvYCnzSwWC+dR\nBoAAtfEzlJufJ/nS3F82fK1fp8uCutx1RpKUbGv564lwfuRAcZo4AJKPN/N58/777xuxDgCIa30d\nUvFxqbXzRfhzMZH6U8e9UlSgdbvnSZJuGDyn1Q2M9iktf89g5pETAaEMQApiRxkAELhBHVufEw7k\ninuBXO461Sad39X7cZhHbhlzygAkQhkAIsJmkbLbeP9aMJel9jeWO6VKnZvNJzOP7BtzygAkQhkA\nIubCHqePQQQTyfV8xXKqTRrRreljmEf2H+MXAAhlAIiQNLs0oquU/P1P3lAiuV5rsdzX4fmoV1ws\njRghLVvmCeM33pAef1wJcRGRYBDKAPjxCAARNLSTdEbb8ERyPW+x3CnFravOOHUf5pEDx5wyAEIZ\nACKs8mShCjeGJ5LrNY/ltTvvU5LVzTxyCJhTBkAoA0AEFW4p1Pjlt8ntdumHg8ITyfUGZF2je89f\nrhRbil76fJ5+tux/NGaMm3nkEDB+ASQ2QhkAIqRwS6Fue+02udwuTbt4mpb8cLrO7iSlB3xG+9Ml\nWaXebaWZF1+j5T9eruTjZ2r+vT/RsmUWZWa6mUcOEqEMJDZ+ZAJABDSP5OmXTJfNKl3dS/phX88l\npu1B/ES2SOqQIl3SQ7o513MVPveOa5T80ibp0GCp03Zd99Svdd11/l3uGk0xpwwkNkIZAAzmLZIb\n69FGuiNPujFH6pMptU3y/cM53S51z5CuyJbuGigNy/LcXj+PXH4iSedfcUDJP7tIfymZrvtW+ne5\nazTFnDKQ2MLwCz8AQEt8RXI9i0XKcXg+quukfeXSzhPSsWqp7vvrXtssUnqS1DvTc/GStsmnHl9e\nLt15p+fUb5JnHnnKlC5atfMVFRQWaF6R53LXc65t/XLXON0ll0grVnjGL267LdqrARBJhDIAGMTf\nSG4uxSb1cXg+/FFcLBUUeE79lpkpLV586tRv1/TzzCwTy8FjThlIXIxeAIABgo3kQPlzfuT6WE6x\npWhe0TzGMALEnDKQuAhlAAizSERyoOdHJpaDx5wykLgIZQAIo0hEcnm5NGaMAj4/MrEcPMYvgMRE\nKANAmEQikouLpREjPG/ay8xUwOdHJpaDQygDiYlQBoAwiEQk+zOP7A9iOXDMKQOJiVAGgBAZHcmB\nziP7g1gODHPKQGIilAEgBEZHcrDzyP4glgPD+AWQeAhlAAiS0ZEc6jyyP4hl/xHKQOIhlAEgCEZH\ncrjmkf1BLPuHOWUg8RDKABAgIyPZiHlkfxDLvjWeU2ZXGUgMhDIABMDISDZyHtkfxLJv9eMXvKEP\nSAyEMgD4ychIjsQ8sj+I5dYxpwwkFkIZAPxgZCRHch7ZH8Ryy5hTBhILoQwAPhgVydGaR/YHsewd\n51MGEguhDACtMCqSoz2P7A9i2TvGL4DEQSgDQAuMiuRYmUf2B7F8OkIZSBwx+GMZAKLPqEiOtXlk\nfxDLTTGnDCQOQhkAmjEikmN5HtkfxPIpzCkDicNnKM+dO1dnnXWWHA6HHA6HRo4cqZUrV0ZibQAQ\ncUZEshnmkf1BLJ/C+AWQGHyGcnZ2tp566il9/vnn2rBhgy677DIVFBRo06ZNkVgfAESMEZFspnlk\nfxDLHoQykBh8/qgePXq0rr76auXk5Khfv36aMWOG2rZtq08//TQS6wOAiDAiks04j+wPYpk5ZSBR\nBLSnUVdXp8LCQlVVVemiiy4yak0AEFHhjmSzzyP7o3ksP7X1qYSKZeaUgcTgVyhv3rxZbdq0UWpq\nqiZMmKAlS5ZoQDz9xAeQsMIdyfEyj+yPxrH8191/TbhYZvwCiH8Wtx8/1ZxOp/bs2aPS0lItXbpU\nzz33nN5//33l5+c33Ke0tLTh8x07dhizWgAIo1X7Vmnq51Plkkt3596tCf0nhHS8PXtS9NBD/bRz\nZ5oyMmr1m9/s0oUXlvp+oMmtPbhWkzdMVo2rRj/q9SM9POhhWSyWaC/LcNu2pWv8+IHKzq7Sa69t\nifZyAAQpNze34XOHw9Hka36FcnNXXnmlevbsqQULFjTcRigDMJNwR/LHH2dq6tQclZXZ1bt3pWbN\nKlbv3tVhWm3sS8RYrq2VrrjibFVU2PXmm5vUpYsz2ksCEITWQtkezAHr6urkcrla/HrjnWbAjIqK\niiTxdzleFW4p1NSNnkgOddzC7ZaeeEKaMsXz+Q03SC+/nKbMzCHhW7AZFEmzfjBLD3/2sP66+6/K\n6pylOdfOiftYvuQSacUK6fjxs3TdddFeDcyG15rY0HiztzmfM8qPPPKI1qxZo2+++UabN2/Wo48+\nqg8//FDjxo0L6yIBIBLCOZOcSPPI/hiZNTLhzobBnDIQ33zuKB84cEDjxo1TSUmJHA6HzjrrLL39\n9tu68sorI7E+AAibcEZycbFUUOA59VtmprR4cXyc+i1U9W/wKygs0LyieZIU1zvLhDIQ33yGcuM5\nZAAwq3BG8ltvSbfe6jn1W16etHx5fJ36LVSJFMvNz6fcs2e0VwQgnEx6bSgA8F+4IjkRzo8cLoly\nURLOpwzEN0IZQFwLVyQzjxy4RIllxi+A+EUoA4hb4Yrk4mJpxAhp2TJPGL/xhvT445KVn6A+JUIs\nE8pA/OLHPIC4FK5Ifustafhwz5v28vKkTz/lTXuBivdYbj6nDCB+EMoA4k44Ipl55PCK51i226X/\n+A/P58wpA/GFUAYQV8IRycwjGyOeY5nxCyA+EcoA4kY4Ipl5ZGPFaywTykB84kc/gLgQjkhmHjky\n4jGWzzlHatuWOWUg3hDKAEwv1EhmHjnymsfy/W/db+pY5nzKQHwilAGYWqiRzDxy9DSO5bnr55o+\nlhm/AOIPoQzAtEKNZOaRoy+eYplQBuIPLwcATCnUSGYeOXbESywzpwzEH0IZgOmEEsnMI8emeIhl\n5pSB+EMoAzCVUCKZeeTYFg+xzPgFEF8IZQCmEUokM49sDmaPZUIZiC+8RAAwhVAimXlkczFzLDOn\nDMQXQhlAzAs2kplHNi+zxjJzykB8IZQBxLRgI5l5ZPMzaywzfgHED0IZQMwKNpKZR44fZoxlQhmI\nH7xsAIhJwUYy88jxx2yxzJwyED8IZQAxJ5hIZh45vpkplplTBuIHoQwgpgQTycwjJwYzxTLjF0B8\nIJQBxIxgIpl55MRillgmlIH4wEsJgJgQTCQzj5yYzBDLzCkD8YFQBhB1gUYy88iI9VhmThmID4Qy\ngKgKNJKZR0a9WI9lxi8A8yOUAURNoJHMPDKai+VYJpQB8+PlBUBUBBrJzCOjJbEay8wpA+ZHKAOI\nuEAimXlk+CMWY5k5ZcD8CGUAERVIJDOPjEDEYiwzfgGYm89QfuKJJzR8+HA5HA5lZWVp9OjR2rp1\nayTWBiDOBBLJzCMjGLEWy4QyYG4+X3I+/PBD3Xffffrkk0/03nvvyW6364orrtCxY8cisT4AcSKQ\nSGYeGaGIpVhmThkwN5+h/Pbbb2v8+PEaOHCgBg8erFdeeUWHDh3S2rVrI7E+AHHA30hmHhnhEiux\nzJwyYG4B/xLzxIkTcrlcat++vRHrARBn/I1k5pERbrESy4xfAOYVcCg/8MADOuecc3T++ecbsR4A\nccTfSGYeGUaJhVgmlAHzsrgD+Inx4IMPasmSJVqzZo169+7d5GulpaUNn+/YsSNsCwRgTqv2rdLU\nz6fKJZfuzr1bE/pP8Hq/jz/O1NSpOSors6t370rNmlWs3r2rI7xaxLu1B9dq8obJqnHVaEyvMZo8\naLIsFktEvndtrXTFFeeoosKmN9/cpC5dnBH5vgD8k5ub2/C5w+Fo8jW/92smTZqkV199Ve+9995p\nkQwAjfkTyW63tGBBV02alKuyMrsuvviYFizYTiTDECOzRmrWD2Yp2ZqspbuXatbWWRHbWbbbpbPP\nLpMkffZZ24h8TwDhYffnTg888ICWLl2q999/X/379/d5//z8/JAXBkRTUVGRJP4uB6NwS6GmbvRE\nckvjFuXl0p13ekYtJM888pQp7WW18t4HszLDcyZf+erfv78KCgu0dPdSZWVl6blRz0VkZ/mGG6SP\nP5a+/TZH+fk5hn8/mIMZnjeJoPFURHM+d5QnTpyohQsXavHixXI4HCopKVFJSYkqKirCukgA5ufP\nTDLzyIimaM0sM6cMmJPPl6bnn39e5eXluvzyy9W9e/eGj9///veRWB8Ak/Ankjk/MmJBNGKZ8ykD\n5uQzlF0ul+rq6uRyuZp8PP7445FYHwAT8BXJnB8ZsSbSscz5lAFz4pedAELiK5I5PzJiVaRjmfEL\nwHwIZQBB8xXJzCMj1kUylgllwHx4uQIQFF+RzDwyzCJSscycMmA+hDKAgLUWycwjw4wiEcvMKQPm\nQygDCEhrkcw8MswsErHM+AVgLoQyAL+1FsnMIyMeGB3LhDJgLryEAfBLa5HMPDLiiZGxzJwyYC6E\nMgCfWopk5pERr4yKZeaUAXMhlAG0qqVIZh4Z8c6oWGb8AjAPQhlAi1qKZOaRkSiMiGVCGTAPXtYA\neNVSJDOPjEQT7lhmThkwD0IZwGm8RTLzyEhk4Yxl5pQB8yCUATThLZKZRwbCG8uMXwDmQCgDaOAt\nkplHBk4JVywTyoA58FIHQJL3SGYeGThdOGKZOWXAHAhlAKdF8rSLpzOPDLQi1FhmThkwB0IZSHDN\nI/mh/OnMIwN+CDWWGb8AYh+hDCSw5pE8rud05pGBAIQSy4QyEPt4+QMSVPNIPq9yOvPIQBCCjWXm\nlIHYRygDCahxJD9+0TQlr53OPDIQgmBimTllIPYRykCCaRzJj577W22dO515ZCAMgollxi+A2EYo\nAwmkcSTf3++P+vvkXzKPDIRRoLFMKAOxjZdEIEE0juRbU1/RK/f9gnlkwACBxHLzOeVduzz/aA3y\ngn8AwoxQBhJAQyS7XLpsz2r95dFxzCMDBvInlp1OyWZrOqd8xx3S6NGecAYQfYQyYBIul0uHDh3S\nzp079eWXX2r79u3avn27vvrqK+3atUulpaVyu936ZM8nmv7BdFXXVktqFMlVaTrz/a1678XLmUcG\nIqC1WJ79wf9T+87V+s//lM49t0aS9NprpVq3zi2r1a0TJ75ScXGx9u3bJ6fTGc0/BpDQ7NFeAIDW\nuVwu7dmzR2VlZaqurm7xfseOHVNKSoruWH2Hij8ZpDcedeonv3lHv1h7m1yH+6jzG2u1/ZssZWZK\nixczagFEQn0sFxQWaO76uZKk50Y9p+kf/3+qqLtcK1Z0U1lZpaRkrVmTqtpai3JzT0oqU2mpVFpa\nqsOHDys9PV3Z2dlKSUmJ6p8HSDSEMhDDTpw4oT179qiqqsrnfd1ut7Yc3KLiw7tleesDfXaijT4v\nnCV31VVKff01HSpPU16etHw5oxZAJDWPZbfbrfxug/Tu2NGyL/pYH33kUFKSSwcPeiJ48OCKJo93\nOp0qLS1VZWWlOnfurK5du0bjjwEkJEYvgBh15MgRffPNN35Fcr3V+1dLW8bKfaKH1HmL3Dsvkv5v\nparK05hHBqKo8RjGvKJ5OlF5QupRpDNu/6UsFreczlMvx0OHVng9Rk1Njfbv369vv/02UssGEh6h\nDMSg8vJy7d27N6DZRLfbrXf3/kP6eLLnBnul9P5vJbdF/3HHu3p1qZN5ZCBKXvzsRa36epUeGPqA\nkixJ+tfhf8kqq3ZlP6P/uvfLJvcdMqS8xeO4XC4dOXJE+/fvN3rJAMToBRBzXC6Xvv3224DfwPN1\n2dfa/flA6dBgyeqU9g+Xkkuli36jNYcr9Y91g3Tthd0NWjWA1vzu49+p+KjnVBaZSZmqc9bJJZck\nyXHxS7p026N6//32stnc6tWr5fciSKfe2Nu+fXulpqYavnYgkfm1o/zRRx9p9OjR6tmzp6xWqxYt\nWmT0uoCEtX//flVWVgb8uJeKX5JWP+H5D1eSZ0e5Nl1a/bS0cq6e+CWRDETLyrErdVf/u9QjvYdO\nOE80RLIkvbLzZc2cuVPDh5/QrbcekMXi+3hOp5MRDCAC/NpRrqio0NChQzV+/HjdcccdsvjzLAYQ\nMLfbrdLS0qAeu7Gog3RoyKkbatNksbiV07dSw4Y5NXkycxdAtDhcDk3sP1H35t6r7aXbtXr/ar25\n500drTmqI9VHlJQkPf/8joCOWVlZqerqas6EARjIr1AeNWqURo0aJUm68847jVwPkNDKy8sDevNe\nY8+M+pl+tqRcWR2sunbUUQ0ZUqEzzzypjAyXkpOTNXjwYEn8IxeIhmPHjkmSLBaLBrYbqIHtBur+\nvPu19tBatU9qH9Qxa2trVVJSol69eoVzqQAaYUYZiCHHjx9v8VK3vuRlt9WHq7/0+rXa2lpVVlYq\nPT09lOUBCILb7fb6ngOLxaILsi4I6ditnVsdQOg46wUQQ4x60XO5XCorKzPk2ABa53Q6VVdXZ8ix\na2trDTkuAA+LO8Dtq7Zt22ru3Lm64447mtzeeK5yx47A5qwAnGLUewCC3akGEB5GPLd5XgOhy83N\nbfjc4XA0+Ro7ygAAAIAXhswo5+fnG3FYIGKKiookRf7v8q5du3T06FFDjt2vXz+1a9fOkGMD0XrO\nmIHL5dLWrVtVU1MT9mO3adNGeXl5YT8uIoPnTWxo7WxTfp8ern6cwuVyaffu3dq4caM6duyo7Ozs\n8KwSgNq2bWtIKCclJalNmzZhPy4A36xWq+x2uyGhnJSUFPZjAjjFr9GL9evXa9iwYRo2bJiqqqo0\nbdo0DRs2TNOmTTN6fUBCadeunSEvfMnJybLbOckNEC1paWlhP6bFYlGHDh3CflwAp/j1ynnJJZfI\n5XL5viOAkNjtdqWnpwd90ZGWtG8f3HlaAYRHt27dVFpaGtazVKSmpjJOBRiMN/MBMaZHjx5h3f1N\nS0tT586dw3Y8AIFLSUkJ6/hT/W4yV8oFjEUoAzEmLS1NHTt2DMux7Ha7srOzZbXyVAeirVevXmG7\n3HSbNm3UpUuXsBwLQMt49QRiUI8ePZSZmRnSMaxWqzp37qy2bduGaVUAQmG323XGGWeE/D6E1NRU\n9enTh91kIAIIZSAGWSyWhtO5BbMbnJSUpK5du6p79+4GrA5AsDIzM9W7d++gd5bT09PVv39/znYB\nRAihDMQoi8Wivn37Kjs72+8XVYvFovT0dPXt21fdunUzeIUAgpGZmam8vDw5HA7ZbDa/HpOUlKTO\nnTsrLy+PSAYiiPNFATGuU6dO6tChgw4ePKjjx4/L6XSqtra24Uw0NptNdrtdycnJ6ty5s9q1a8ev\nZIEYZ7fb1a9fP508eVIlJSWqrKxUbW1tk7NiJCUlyW63q02bNurWrRuBDEQBoQyYgNVqVdeuXdW1\na1e5XC5VVVXJ6XTKYrEoOTlZKSkpxDFgQunp6crJyZEkOZ1OVVVVyeVyyWazKS0tze8dZwDGIJQB\nk7FarUpPT4/2MgCEWVJSErvGQIxhRhkAAADwglAGAAAAvCCUAQAAAC8IZQAAAMALQhkAAADwglAG\nAAAAvCCUAQAAAC8IZQAAAMALQhkAAADwglAGAAAAvCCUAQAAAC8IZQAAAMALQhkAAADwglAGAAAA\nvCCUAQAAAC8IZQAAAMALQhkAAADwglAGAAAAvCCUAQAAAC8IZQAAAMALQhkAAADwglAGAAAAvPA7\nlOfNm6c+ffooLS1N+fn5WrNmjZHrAgAAAKLKr1B+9dVX9d///d+aMmWKNm7cqJEjR2rUqFHas2eP\n0esDAAAAosKvUH7mmWd011136ac//akGDBig2bNnq1u3bnr++eeNXh8AAAAQFT5DuaamRp999pmu\nuuqqJrdfddVVWrt2rWELAwAAAKLJZygfPnxYdXV16tKlS5Pbs7KyVFJSYtjCAAAAgGiyG3HQ0tJS\nIw4LRExubq4k/i4D/uI5AwSO503s87mj3KlTJ9lsNh04cKDJ7QcOHFC3bt0a/jslJUU2my38KwQA\nAAAMZLPZlJKSctrtPkM5OTlZP/jBD7Rq1aomt7/77rsaOXJkw3+npqbKbjdkgxoAAAAwjN1uV2pq\n6um3+/PgBx98ULfffrvOPfdcjRw5Un/6059UUlKie+65p8n9UlNTvX4TAAAAwGz8CuWbb75ZR44c\n0YwZM7R//34NGTJEK1euVHZ2ttHrAwAAAKLC4na73dFeBAAAABBr/L6ENZAouFw74L/p06fLarU2\n+ejevXu0lwXEjI8++kijR49Wz549ZbVatWjRotPuM336dPXo0UPp6em69NJLtW3btiisFN4QykAj\nXK4dCFxeXp5KSkoaPjZv3hztJQExo6KiQkOHDtUf//hHpaWlyWKxNPn6k08+qWeeeUZz5szR+vXr\nlZWVpSuvvFLl5eVRWjEaY/QCaOS8887T2WefrRdeeKHhtv79++tHP/qRZs6cGcWVAbFp+vTpWrZs\nGXEM+KFt27aaO3eu7rjjDkmS2+1W9+7d9Ytf/EKPPvqoJKmqqkpZWVl6+umnNWHChGguF2JHGWjA\n5dqB4OzcuVM9evRQTk6Oxo4dq127dkV7SYAp7Nq1SwcOHGjyupOamqqLLrqI150YQSgD3+Ny7UDg\nRowYoUWLFumdd97R/PnzVVJSopEjR+ro0aPRXhoQ8+pfW3jdiV1cIQQAELRrrrmm4fPBgwfr/PPP\nV58+fbRo0SJNmjQpiisDzK35LDOigx1l4Hv+Xq4dQMvS09M1aNAgFRcXR3spQMzr2rWrJHl93an/\nGqKLUAa+5+/l2gG0rKqqStu3b+cfl4Af+vTpo65duzZ53amqqtKaNWt43YkRtunTp0+P9iKAWJGZ\nmalp06ape/fuSktL04wZM7RmzRotWLBADocj2ssDYs5DDz2k1NRUuVwuffXVV7rvvvu0c+dOvfDC\nCzxnAHlOD7dt2zaVlJToxRdf1JAhQ+RwOOR0OuVwOFRXV6ff/e53GjBggOrq6vTggw/qwIED+vOf\n/6zk5ORoLz/hMaMMNMLl2oHA7N27V2PHjtXhw4fVuXNnnX/++Vq3bh3PGeB769ev12WXXSbJM3c8\nbdo0TZs2TXfeeadeeuklPfzww6qsrNTEiRN17NgxjRgxQqtWrVJGRkaUVw6J8ygDAAAAXjGjDAAA\nAHhBKAMAAABeEMoAAACAF4QyAAAA4AWhDAAAAHhBKAMAAABeEMoAAACAF4QyAAAA4AWhDAAAAHjx\n/wPXi/povmdU7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xafdbb78c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as plt\n",
    "from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow\n",
    "\n",
    "landmark_bg = '#CCCCCC'\n",
    "robot_bg = '#88CCFF'\n",
    "particle_bg = 'r'\n",
    "arrow_bg = '#88FF88'\n",
    "\n",
    "#plt.figure(figsize=(8,8), facecolor='w')\n",
    "#ax = plt.axes((0, 0, 8, 8), xticks=[], yticks=[], frameon=False)\n",
    "#ax.set_xlim(0, 10)\n",
    "#ax.set_ylim(0, 10)\n",
    "plt.xlim([0,8]);\n",
    "plt.ylim([0,8])\n",
    "\n",
    "ax = plt.axes()\n",
    "\n",
    "plt.scatter ([1,1,7,7], [1,7,1,7], s=512, color=landmark_bg)\n",
    "\n",
    "# actual\n",
    "plt.scatter ([4], [4], s=1024, color=robot_bg)\n",
    "ax.annotate('', xy=(5,4.5), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec=robot_bg,\n",
    "                            #shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "\n",
    "ax.annotate('', xy=(1,7), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(1,1), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(7,7), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(7,1), xytext=(4,4),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='g',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "\n",
    "# particle\n",
    "plt.scatter ([6], [5], s=1024, color=particle_bg)\n",
    "ax.annotate('', xy=(7,5), xytext=(6,5),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec=particle_bg,\n",
    "                            #shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "\n",
    "ax.annotate('', xy=(1,7), xytext=(6,5),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='b',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(1,1), xytext=(6,5),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='b',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(7,7), xytext=(6,5),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='b',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "ax.annotate('', xy=(7,1), xytext=(6,5),\n",
    "            arrowprops=dict(arrowstyle='->',\n",
    "                            ec='b',\n",
    "                            shrinkA=3, shrinkB=4,\n",
    "                            lw=2))\n",
    "\n",
    "plt.axis('equal')\n",
    "#plt.axis([0,8,0,8])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The closer a particle is to the correct position, the more likely will be the set of measurements given that position.\n",
    "* The mismatch of the actual measurement and the predicted measurement leads to an **importance weight** that tells how important that specific particle is.\n",
    "    * The larger the weight the more important it is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Next we allow the particles to survive at random, but the probability of survival will be proportional to the weights, called **resampling**.\n",
    "    * A particle with a larger weight will survive at a higher proportion than a particle with a small weight.\n",
    "    * This means that after resampling, which is randomly drawing new particles from the old ones with replacement in proportion to the importance weight, the particles with a higher importance weight will live on, while the smaller ones will die out.\n",
    "    * The \"with replacement\" aspect of this selection method is important because it allows us to choose the high probability particles multiple times, which causes the particles to cluster around regions with high posterior probability.\n",
    "\n",
    "From here we need to implement\n",
    "\n",
    "* a method of setting **importance weights**, which is related to the likelihood of a measurement\n",
    "* a method of **resampling** that grabs particles in proportion to those weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importance Weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Program a way to assign importance weights to each of the particles in the list. \n",
    "\n",
    "> The function **measurement_prob** accepts a single parameter, the measurement vector Z, and calculates as an output how likely the measurement is. By using a Gaussian, the function measures how far away the predicted measurements would be from the actual measurements. \n",
    "\n",
    "> For this function to run properly, you have to assume that there is measurement noise for the particles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Assign Importance Weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Make a list of 1,000 elements, where each element in the list contains a number that is proportional to how important the particle is. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = 1000 \n",
    "p = [] \n",
    "for i in range(N):\n",
    "    r = robot() \n",
    "    r.set_noise(0.05, 0.05, 5.0) \n",
    "    p.append(r)\n",
    "    \n",
    "w = [] \n",
    "# write code here to use  measurement_prob to get importance weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resampling is when you are given N particles, each of which has three values and each one has a weight, w. The weights are continuous values and the sum of all of them is W.\n",
    "\n",
    "$$W = \\sum_{i=1}^Nw_i$$\n",
    "\n",
    "Normalize the weights:\n",
    "\n",
    "$$\\alpha_i = \\frac{w_i}{W}, i=1,\\ldots,N$$\n",
    "\n",
    "The sum of all alphas is:\n",
    "\n",
    "$$\\sum_{i=1}^N \\alpha_i=1$$\n",
    "\n",
    "* Resampling puts all the particles with their normalized weights into a big bag.\n",
    "* Then, it draws, with replacement, N new particles by picking each particle with probability $\\alpha$.\n",
    "    * For example, $\\alpha_2$ is drawn and becomes $p_2$, and similarly $\\alpha_3$ might also be large and picked up as $p_3$. By chance you may also pick up small $\\alpha_4$, to add $p_4$, and you can also pick the same one again, like $\\alpha_2$, to have two versions of $p_2$, or maybe even three!\n",
    "* Draw N times.\n",
    "* In the end, those particles that have a high normalized weight $\\alpha$ will occur more frequently in the new set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quiz: Resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During the process of resampling, if you randomly draw a particle in accordance to the normalized importance weights, what is the probability of drawing $p_1,\\ldots, p_5$?\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "p_1 &: w_1=0.2, \\, P(p_1)=(?)\\\\\n",
    "p_2 &: w_2=0.4, \\, P(p_2)=(?)\\\\\n",
    "p_3 &: w_3=0.8, \\, P(p_3)=(?)\\\\\n",
    "p_4 &: w_4=0.2, \\, P(p_4)=(?)\\\\\n",
    "p_5 &: w_5=0.4, \\, P(p_5)=(?)\n",
    "\\end{aligned}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One Possible Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "input_collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lBNTZ6SiNslTEehG6C3+P5/t/i84UzO4drb3uxyFHg5kug2jcYvgl+gBV0/Dy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\nhBBCpAwJv0IIIYQQImX8/zFidVtc029OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaebf084c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# The slices will be ordered and plotted counter-clockwise.\n",
    "labels = 'w1', 'w2', 'w3', 'w4', 'w5'\n",
    "sizes = [10, 20, 40, 10, 20]\n",
    "colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', 'red']\n",
    "explode = (0, 0, 0.1, 0, 0) # only \"explode\" the 2nd slice (i.e. 'w2')\n",
    "\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True)#, startangle=90)\n",
    "# Set aspect ratio to be equal so that pie is drawn as a circle.\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "input_collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xad38e08c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "# Make a figure and axes with dimensions as desired.\n",
    "fig = plt.figure(figsize=(8,3))\n",
    "ax2 = fig.add_axes([0.05, 0.25, 0.9, 0.5])\n",
    "\n",
    "cmap = mpl.colors.ListedColormap(['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', 'red'])\n",
    "\n",
    "bounds = [0, 0.1, 0.3, 0.7, 0.8, 1]\n",
    "labels = 'w1', 'w2', 'w3', 'w4', 'w5'\n",
    "\n",
    "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
    "cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap,\n",
    "                                     norm=norm,\n",
    "                                     boundaries=bounds,\n",
    "                                     ticks=bounds, # optional\n",
    "                                     spacing='proportional',\n",
    "                                     orientation='horizontal')\n",
    "cb2.set_label('Cumulative Sum')\n",
    "#cb2.set_ticklabels(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Represent the cumulative sum of importance weights in a bar.\n",
    "    * Cumulative sum means that element zero is the weight zero, element one is the sum of weights zero and one, element two is the sum of elements zero, one and two, etc.\n",
    "* Then generate random numbers in the range of 0.0 to 1.0 and do a binary search to find the weight that most closely matches that number.\n",
    "* This algorithm is variously called **multinomial resampling** or **simple random resampling**. \n",
    "\n",
    "This is written as:\n",
    "\n",
    "```python\n",
    "    # w: list of weights\n",
    "    # p: list of particles\n",
    "    p3 = [] \n",
    "    cum_w[0] = w[0]\n",
    "    for k in range(1,N,1):\n",
    "        cum_w[k] = cum_w[k-1] + w[k]\n",
    "        \n",
    "    for i in range(N):\n",
    "        u = random.random()\n",
    "        for j in range(N):\n",
    "            if cum_w[j] >= u:\n",
    "                p3.apend(p[j])\n",
    "                break\n",
    "    p = p3\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Other Faster Possible Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "input_collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hBL0k0cVKWrqQDTnjMcA2HWbbcAoQSKdTVRWyLENRlKjr/RqgaipyPNfBJK85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\nJf01LtF2MpXHrmez8gf2ev46bvumOgHJEXsd7yRrjSF9OZXJuN6XM44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2zqZNmzj77LNH\nXNvW1saTTz45IpE/GdlYcn/yezdx3PmXM2/FmUOKclxHm/A6Olm48vzYe6YiC3OWr6Fl+xujtnd4\n+1tDrgFYuPI8ju5+F0WWKa6oobS6gQOvP0c44Kdpy6s0LDoeWZJ46ns3cdk3fhVLp9EyroiAMzgY\nDVBVlXbHPl5pupd3XT/Std3ziKrhper4FZvUIvB63ctEkQM+DMLYv4kynwsOHiSyZk2OrNJJStyg\nf+WVV/LHP/6RP/zhD+zZs4ebb76Zjo4OPvvZzwJwxx13cN5558XOX7duHTabjRtuuIFdu3bx5JNP\n8qMf/YjbbrstY+YN6cupXZGxe09V/F4PVmHskuGFqgOxuRll9mxUfWalDeJ8ujVr1uSkL4/ZJ0tK\nSlixYgUbNmwAos76F77wBVpaWujs7MTv97N582bWrl0bu+ZjH/sYxcXFzJgxA6vVyoMPpreRQKbd\nkref/APOo01c8LnvAEMfSB5HdImipKp2yDUllTV4eoYuf8TjdXYmuKYORZbw9fYgCAIf+9HjbPj9\n97n/I8uZvvgkVlx6Pa/870+YuexUiiuqefAT5/DjDy3hhQczu/FGZhHoDEQdd0kKs7/tFTa03saR\nwH/zbZiOhh34eIZvGJQQfQCaMG6PB7s6elpVnVnCdPQwAEpVFUp5ea5M0xmLOAf+7LPP5v777+d7\n3/sey5cv5/XXX+c///kPM2fOBKLFbIcOHYqdb7fbef7552lra+Pkk0/mi1/8Irfffju33nprVkxN\naTKeQv62ztj4ghGKJfeon4soWHqdoKoooRCRlStzaJ3OqMTlvS9fvjwnfTlpDvzatWvZtGkTX/va\n13j55Ze5+eab2bhxIxs3bqS6uhqj0cipp54aO//+++/nrrvuYt++fdxxxx1cd911PPHEEyn/DZRU\nNotIke7mfTz3y2/xmYc2Ivb/cVVVTamcfqIFQnNOXMXnHx2UDOo53Mg7/3iYLz72Fr//7IWs/MhN\nHHf+FfzymtOZsfRkjj3jogndL1vs6DUwt7iHHu82DnqepMQ8jRKm5dusKY+B0TW/tYRRH/RzQkRW\nMfgciMXWhHKSK81ODLt3AyAcPkzoqquwphlc0ck8qmVoP77pppuGpKjG8/DDD494b9myZbz00ktZ\nsW04xiQrPIBe9JIhwn09mKvKEqryHV8cxLgjmpIr7txJ6OqrMb8xesaATm6I78uCIOSkL6fkwP/i\nF79g7969uN1uVqxYwdq1a9m4cSO1tbWsWrUKY1wUoa6ujrq6OhYtWkRlZSVr1qzh3nvvHSGRkwsO\nb38Lf28P93/4xNh7qizTvPVV3v6/33Hz37YC4HV0UVY3I3aO19lFaVXdiPYGKK2qHxGh9zo7EQ1G\nikfZnfQfd3+ei27+PggCbXu3csKFH8FksbL4zPdz6J1NmnXgjYJC0a79zPrDY8zWl0fzjwBGg5HI\nNRGoTX56PhDFwUHHakxl0Nc3b8sEPQ4HtdZSusSR0fWKgAv8/uhBSwuRtWt1B14DqLWDnTi+32iF\n+ECWxRBdURtrvwEhCzVsUxGX20tDSTfd5toRuysvUR2ILc3Rg3AYZcECVPTkpXwT35dzpRCV1IFf\nvXo1oVCIe+65hzVr1iCKImvXruWTn/wk9fX1XHTR6I6nLMtD/psKYiqz/BRZevYHmbHs5ME3VJW/\n3/kpqmctZO0nvkr1rIWUVNVz4M3nmb7kJAAioSDN217j4lt+NGq7s44/jV0b/znkvQNvvsiMpSfH\nIv3xbP7nIxQVl7Ds3MsJeHoBkKUIJqxIkTCChh2Yk8Uu5FAI+5+fQAjqOe9aQX2fNid8MPThZTOk\nkkKjPcelEJEVFVdnK1UNRhwMKmhVmWXMrW1DzlVqa1FLShC83lybqdOPajCg1tTEjnMtC5kKgiAg\nCEKsdsxqUPHJY9ip4bGs0OjuaKdmhplusTzOiVewup2gDObIK4qCvGIFxnffzY+hOgAocWoxuerL\nSUfOgTz4P/3pT7Fi1dNOO43W1lbefPPNWP77M888wyOPPMLOnTtpbm7mmWee4bOf/SyrVq1i7ty5\nKRuUya9tKS2jbt6Swf/NX4rJYsNqr6Bu3hIEQWD1ui/y0h9/zK4N/6CjcSd///YnKLKVcuJFH421\n89dv3shfv/Xx2PFpV34ad1cb//7x7XQd2sM7Tz3Eln8/ypprR+YreZ1dbPjd3Xzwjp8DYC0tp3b+\nEl559D7a9m5l54tPMefExJXJWqDc44CuLoJjTNR08oAGo3UDxD+8rIYU8uDT1MzVGZ1gKIK7vZka\nBh3z04tcGHbtHHpiezuhD30ot8bpDEGtqYn99gccZS0Sb1dxkhU1WTQgavR7FBqSotLd2kKd2ht7\nb6ktjPHAUOU3cft2gtddl2vzdIahxm3clKvVtJTusnbtWmRZjjnrFouFlStXYrFYYvnvFouFBx98\nkDVr1rBkyRJuu+02PvjBD/LMM8+kZ1CW+74gCEOK5s664XZWX/0l/vnDm/nltavwOrv4+K+ewWwd\n3MGor7OVvo7W2HHFtDnc8MDTNG15hZ+vO5VND9/DpV+5n6XnjBwQ//3j/2HNtbdirxnMG//wXQ+x\ne+PT/O4zF7Ls3MtZdu5lWfq2E6PaLFPU1Y5h3z7Cl1ySb3N04tG4Az8kCp9k0BdEzW0IXdBYi4rw\nCIP5mFUBJ3g8Q84RGhsJ65PyvBI/4GvVeYehzkiyFTVJMGIyaPe7FBpms5GAYI0dHyf0IMYVPwIQ\nDCL3p9Ho5AfVaMxLCo2gqtraIPmxPX6OBLUvsTgVuNjawexn/gpeL8rKlZRfeilCeKQuvE7uCf7n\nP1g07IB5vd5Y6tyTR4voDI0eZa/Aj+vwfmR929YJU2QyUDp9Aa7+7T3LjAof6XwV42uvjjhXXbOG\nsg98ACFQGJuCTTYi73sf/r9E9xsxGo0UF4+x7XUe8fv9RCLRfQZe7jaxyzP6+GwVFdSju/GFCmOf\nCi0jCgK1M2fTLQ7sXaPwcWUXRf/+14hzldNOo+T22zHt2pVbI3UAUKZNwzMgEiAI2O32nNxXc2E8\nK3rH1wrVARf058iqHg+h889PcoVOzrDZ8m3BmKSTBx8WzZgMmnsUFRyCAJV103AxGLFbZe3FuGN7\n4gu6uwm///05sk5nOAUZgU+ymhZRBUxmc7ZNmhLU1tXgiCtIX2SNYGw8kPBccedOgtdfnyvTdIaR\nj/x30KADbxGkfJugA5QbFUxdg1v7GvbsIfTBD+bRIp0haNyBT2fQlzBgMuurbhOlpqoKh7GS+Eqi\n2oAD3Ik1pYW9ewlfemmOrNMZjhK3rboWFWgGGJIDnyyFRkV34DNAub0Yj7VuiOLPctGBobEx8QU+\nH8rixTmyTmc4apwDn8u+rLmnhkVIXbFGJ3usLHJg3h23HKcoKDNnouoFh3lHBc078OkM+hEFzOai\nbJs0qSktthIsrUeOe6SXGBXMPV1jXqdMm4ZaALtCT0YmYwQeBASj/nuaCEUmA6bK6QTV+NoghWKv\nC6TRA5xKURHyggXZN1BnBEqe+rLmHHgU3YHXAjX+kZE71e8nHLfrrk6eKClBsGh7I6d0Bn0FAYNJ\nj9qNF6MoYKuejk8d+jdcae0bPX2mH7Wvj/CFF2bTPJ1RKMQIfEqysAa9L48XgWganJOhAZp5VglT\n86HEF/Uj7tpF8MYbs2idzmioeerLmntqCIqeQpNvRovcGfbsIXjllXmwSCcetaoKoYAi8PZUNnMy\n6hH48VJT34BDKBnx/rSQE1yuMa8Vdu0irPfpvKDEbW5YKBH4aF9Ooipl1B348VJTXTkiDQ5ghehA\n3Ldv7Iv7+pCWLcuecTqjosyeHXs9tSPwqh6Bzzcri5yYdieoZpcklNmzUTUcLZoKKFVVCKWl+TZj\nTAxxqVbVRQr6oJ8dqirKcBXVjNit0SomT58Boqlx06ejGnUpz1yi2mwoxxwTOzZoODUxXha2yJB8\nQh42WPSi9HFQarMQKm0YkgY3QInXCSkowKk2G3KcM6mTG+QTToi9zmVf1l4v01No8k590IkwSuRO\nDYeJrNLuxlNTAbWhQfMpNEMGfRHKUhr0tRuF1CI2iwm1bBoRdeRj/DSbG+POHSm1o3i9RPTUuJwi\nL1sW28RJFEVNR+AFQRjilNQUKWOcDSHBjKVIz4NPB6MoYKuZjlcdGciYYYlgOtKSUjvC3r0Eb7gh\nw9bpjIVaXIyyaFHseEo78KIcIVm0Tid72ESFIsfokTvDrl0ErroqhxbpDEc57jhN58zC+AZ9q0VP\no0kVURCw106nj8QTuZlhJ0JPT2pt7dpFSO/TOUU+8cTYay1H3wdIqy8rYLFqO8VPa9TU1eMQEq+q\nnmZ0YNi7N7WGHA6k5cszaJlOMuTjjottrJjrybjmvICAz5NUtUIne5xq7cXUvyFBQiIRlHnz9ClW\nHlGPPVbTEbsB0h30i/RBP2WGa0THYxYVilypOe+AnhqXBwragTeP3ZdBQDBbk5yjM0BVRRm9ltoR\naXAD2H0uCAZTbk8tLUWZNi35iToZIZ99WXNPbLfLQblRT6PJFzNCySN3qiwjnXxyjizSGUFNzaRz\n4PVBP3XK7SUjNKLjOdXmxbhrZ1ptKqEQkTPOyIR5OilQ0A58KjUtJm2n+GmFaBpcA+EEaXAAdWYJ\n09HWtNoU9u0jeN11mTBPJwXkuBUP3YH3eKgUU59t6mSOIlHB7OxOep5h924CV1+dA4t0ElJVlW8L\nUiLdQlZM1lFcUp0BEmtED2Wu5EDo6Bj180SI27cTWrduoubppEAhFbAOkG4ha8hopcioOfdCUwym\nwY0euDjd7MCQSFBiLLq6iOgBtpyRrwJW0KADHwyFsRLKtxlTklOsHkx7xkifGSAYRF6EWUeQAAAg\nAElEQVSwQE+jyQNqcTFCgTjw6RayBg02rEW6GspoCMKARvToA74RhSKXI/3GIxGUuXP1Pp0DCqmA\ndYB0a1oCqoniYj0lbixqa6tHTYMboNzfC35/2m2rFRXINTXjNU0nRfJZwAoadOABBCm5XJJO5pkd\n7kHs7EzpXFUQosUbOjlFWbAAsUAezGkP+oqIrXiknrlOlJqqqoQa0fGsKPFhTGUSngBFkpBPOWWc\n1umkSqGlzwyQTl+WVAGTtTjbJhUs5fZiPLb6UdPgAGrMMub2o+NqXzh4kOA114zXPJ0UyWcBK2jV\ngZf1CHyuiUbuUi98M+zejV9/QOQceeVKxJLCcXLTGfQVBAwWfdBPRKnNQrC0PqFGdDwLJSfC0fEN\n+uKOHQSvvXZc1+qkTj5zZidCejUtgFnvy4lIJQ0O4PQiJ4Y0a1litLUhr149vmt1UibffVmTDnwk\n4MMs6ou5uWRFsRfTvhSlqgD8fuS4PE6d3KAsX655Ccl44h9qM6zJi9MVkw1R+xkFOWVAI9qXQCM6\nHhEFS58D1HE+O4NB5Pnz9TSaLKIKAtJZZ8WOC9WBry9SMApj/1ICxmKsZj0lLh4BqKxrwEny9KLK\ngAu83nHfS6moQKmoGPf1OsmR4vbP0B34flzdXdSYIvk2Y0oxP9KDmGbkTi0qQjr22CxZpJOQmTML\nImd2AGPcDp/VRSolxrEjdz7RRolNV7CIp6Z+dI3oeJYXBzDs3TOheymiqKfGZRH5hBNQ+yX+hqeY\naR1RFGPBA6OYfELuVwwUa3zH6FxTXV2Jw1jFWGlwABUmBVNn+4TuJbS0EProRyfUhs7oqDbbkMm4\nyZT7zcs058DLioLD6aTe4Mu3KVMGIwqW3vQL3wy7dxPQl9xzhmo0wsyZ+TYjLQRBGOLEz7GNPeiH\nVBFriT3bZhUMVRVl9BaNrhEdz7GKA/Hw4QndT9y5U9/JMYtIF10Ue200GgtqMg5DnZRkfVlFwGAt\nnHS/bFNqsxAuaUiaBgfR9BnjjtR2Uh6Vw4cJ6zssZw3p7LOhf0f0+MltLtGcAx8MhpAVBVMkkG9T\npgwnFPsxppM+M4DHg7R4ceYN0kmIfOKJGGbNyrcZaZOOAw9AkR61gwGN6GmjakTHE02fcY4/fWYA\nnw9ZX1XLGpE4Bz4fEbuJMqQvF8sISRKuIqYSzAbNuRk5ZyANzsvYaXAD1IZ6we2e8H3VmhoUfRUk\nK2ihL2uuZw0sKaohH6KejZkTFkndGFrT2yxiAMVmQ54zJ7MG6SREvuwyDAX4MI5/uE2zKpiT5M76\nDMWUWFMb6CYrgxrRqaUTLS0OYdw/jkl4AhSjETlOGk0nMygzZqAcf3zsON4ZLhQMBkNs1cBqgNok\nxaxeiii1F94zK9PU1KWWBgdQZlQwdae3j8OoHD1K+IorMtOWTgxVFJEuvDB2nK++rDkHvtweXT53\ndrVRW6TvyJptJhq5M+7bh1/f9S0nqMuWFVQB6wDxy4sGAWYmicIHVJFi+9j6yJOdVDSi41mm9iC2\ntGTk3uKuXQRvvDEjbekMEnnf+2KvCzF9BtJPiZNVAaNtaqfEVZXb6bXUpJQGB7DS2otx5zjVZ4Yh\nHDxIOO53p5MZ5JNPRu2Xc85nLYvmvIHqynJUVaWnx0GDqOfBZ5tltiCmA/vH34DLhaQXvWUdVRAK\nLv89nnRyZ0EASykF6N9khFQ0ooeiYHO7QM5QwMPtRlq2LDNt6cQYnv9eqAzpy8XJf3NhcylFJs25\nGjnBWmRCrZhGWE3dwasPucDpzJgNSk0Nqk3fVCuTaKWWRXO9atH8OQSCISKSRJGc/g5kOumxWO7G\nMMHInWq3o8yYkSGLdBKhHHss4iRx4GfZkufOusUS7LbRdxydrKSqER3PYlsEY+MEJuEJUKxWlNmz\nM9rmVEYtLUVasyZ2XIj57wPETz4qzSr2JMpSHtWMvWzqraiJApTVTadPTf05ZhMVzD1dmTWkq4vQ\npZdmts0pjhby30GDDvycGdMHZzNhPQ8+uyjY3E5QUtiUYwwMjY349E2dsop0+eUYKyvzbca4id+l\nzmKAesvYv7mwKmKxTy0N46hG9LSUNKLjOUHoQTx4MKO2iLt2Efj4xzPa5lRGOvtsMEfrOvKlWJEp\n0k2jUREw2MqybZbmqK2tSSsNDmBlsRvDzgmqzwxD2LeP0CWXZLTNqYw8dy5KXKF/PlfTNPcUsVkt\n2Euj0lPdR48wrSicZ4smL4ttIUwHD0y8oe5u5JNOmng7OqOirl5d8IN+fKRiYUnypXfJYsc0hRQs\naqorcRgrSaYRPRQFm8eZufSZAVwupLiCS52JEb7yytjrQo6+DxD/HRak0Jc9xlJKi6fO/g7l9mK8\naaXBRZkRdiL0pL4jeso0NKAWFWW+3SlIJK4oON+1LJocHasqonnwXT09TBc9+TZn0nKc0oWhqSkj\nbSllZSi1tRlpS2coSkUFwsKF+TZjwsQP+otKpKRqNG7VQnnF1Fh6L7VZCJWmphEdz3yLhOlQZqPv\nAyilpSj9mw7pjB+loQHp4otjx5PBgY+POtZZFKrNY6+oBRUDxVNkRc3cnwYXSCMNDsAiKpid3Vmx\nSXU4CMelfeiMD9VgIHz99bHjfPdlTTrwxy9ehC8QQFVVDCFP0nxZnfGgYPP0ZixyZ2huJrBuXUba\n+gFwClAG1AKXArsSnHcnMB2wAWcDu1No+yVgBWAF5gMPDvv8eWBR/72vA+L3A/b2f5bKfTKJdO21\nGOfOzfFdM4/BYIitIphEWFQqjXm+goBoK5/0xawxjWg1fenMFYZuxAMZWEVLgLhnD8G4wUpnfISv\nvx76HV6DwVBQu6+OhiiKQ5yXZfbkO6eHLeWYJ3kxqwBU1TWknQYHcGqxB+OuRCPdxBF27yZ8+eVZ\naXsqIV14IWp/LdrwVeV8oMneNG/WTCzm6HJPd9sR6s3JHw466bHAGsHUlMHIXXs7kdNOy0hTLwFf\nAN4ANgBG4DzAFXfOj4D7gF8A7xB19M8n6mSPRhNwMXAGsA24A/gi8GT/5wqwDvhc/703A7+Nu349\n8DFgybi/2fhQLrhgUgz6giBgNg86qUvtEiSZnPcZSikvLc6yZfklHY3o4ZR4eyGSpeejw4F0yinZ\naXuKoBqNQyJ2RZMojSG+Ly8okTGLyVbUiigvL9w6nlSorq7EYawivTS4KLPDLoSODOm/D0dVURoa\nort564yb8Cc+EXttNpvzLgWrSQfeaDRQV1ONqqp0dnYy06Cn0WSaE+nCmOHCN6WqCiUDhZbPAtcT\ndZSXAY8C3cDr/Z+rwP1EHfDLgKXAI4AHeHyMdn8DzAB+BhwDfLL/Pj/u/7wHcBB14JcQjfzv6f/s\nbaLR+fUT/XJpotrtsGBBju+aPeIH/UqzyrQkxawRVaTIXpVts/JGuhrR8cy2RDC2ZCYFbjT01LiJ\nIV18MWpDAzCy+LPQGb6idmzJ2CtqKgKqrQKDODmX1EpsFsIl6afBARhRKHJlJ31mAMXjIXzeeVm9\nx2RGnjcP6dxzY8fxY1m+0KQDD3DC0mPwB0Ioqoox7CFZpE4nHRRKPK6MR+4MR44QuOqqjLYJ4CYa\nHR/IoGwCOoEL4s6xAGcy6OQn4o1h19B/vBmQgRqgAfgv4AdeBk4AJODTRNNtcr1gFrnqKkzz5+f4\nrtkjcRR+bDzmcuyTsABuPBrR8Zxs7MGwb1+GrRqK0NhI8Nprs3qPyUzok5+MvdZCxC6TCIIwZEUh\npRU1sZiK8smnSGMUBUpqpuNlfE7dKSU+jLuzkz4zgLhzJ6EsjM9ThXCcKpfRaNSEqET+LRiFhXNn\nYzZFoxXOjlbqzMkHep3UmGOVMGUjcnfkCJEzzsh4szcDy4HT+48HFhnrhp1XG/dZIjoTXFNH1EHv\nIbro+Vfgu0Qj/yuAG4F7gdOIOvhnEs2Dv2sc32M8KO9/P+IkSJ+JJ96Bn1ssYzMkL4CzlVdn26yc\nIgpC2hrRw7F7XRAKZdCqBLS3I2UoNW6qIS9ahHzmmbFjLUTsMk18DnC5WWW6dey+LKsChtIqJlsQ\nvqaunp5xpsEBLJCdCG1tGbQoAYqCOmMGqgYcz0JDtVqJXH117FgrfVmz/5Imo5H6/jSatvYO5hjd\n+TZp0rCCboyNjVlpW6muRi3LXITlNqJR9f8jtazCiY4Lq4mmyxwCfg40A78H7gGuJurQbwH+Avxn\ngvdKhlJZibB48aSK2sHQQj6DAItLkxdSe80VlNomTxS+trY6bY3oeKYVSZhaD2fQotFRKitRKqaG\ngkgmic+X1UrELtMMX1FblsKKWq9YOqmi8BNJg4P+9JleR4atSozi9xOJm1TqpEbk8stR+5+Boihq\nJhVO00+UJccsIBAMoSgKgs+JKYnsnE5qlHidWYvciR0dBDJU7X4r8ATRQtY5ce/X9/+3c9j5nXGf\nJaKekRH6TqJFsqPFdz9D1HkXiDruHwVKgEv67comkdtuwzSJ8t/jiR/0l9ilpBu2BVQjxRU12TYr\nJ4xXIzqelSYHht250UMSmpsJZUhhaqqgFhcT/uhHY8eTqXh1OPF9eY5NpjjJipqkChjs1YiTIDBh\nLTLBBNLgAE4sDmDYsyf5iRlA3LGD0Mc+lpN7TSa0Vrw6gKYd+MUL52Hqn+k0N+5jkcWXZ4sKnxmW\n7EbuhKYmwmefPeF2bmbQeV807LO5RJ3x5+LeCwKvAqvGaPN0ooWo8TxPVLIy0eP3YaAUuIJoDj4M\nykqG4t7LBqogoJx77qSM2kF06f3/t3fn8XHV5eLHP+fMObNnTyZL06Qr3WgL3VcIqxQoVilYRBBk\nURTFH3DxxxUvXEWEKle9ellk1Yt68Ypef16xV69tQSiC7HSDNqWle7O1WSaznvP7YzLTSZo0k2T2\nPO/XKy+YSWbON4HvnOc85/k+3+iHoFszE9rYqcNaQqFr+CUn2WC4PaL7KuxqA58vSaMaxL59krUb\nosA110DPnUhVVfOii9RA4u+oqQrMLk4sC19Wmtt7PKgKFFWO4egIyuAAphgtqHvTczeNUAijvh4z\nSwLQXBBavrzXRpWZbh0ZL6ujA6uuM25sDWHDoLPLS3H4KLKYdWTmKUfQU7zwzaisxHQNv/Xfl4Cn\ngZ8T6cd+qOcrevmmAF8l0kryt8Bm4BoiwXZ8nvBqIl1mor4A7CeS2d9GpDTmp8Dt/YzhCPBN4KGe\nx8VEut18D3iLSElP8qv9jwuuXIk+Y0YKj5BZfW+9zysJDpqF95kWnGWeEZdJZUqkR3TNsHpEx/NY\nQ1gP7kvOoBJkVFQktTQun5mFhfhvuy32OJsydqkSf4fh1MIQbm3wWngKKtAtuft38VSMrAwOQMXA\nfqwFzPTFNUYoRGDx4sF/UGACvrvvjj3WdT2rkmrZM5IBLJ0/B58vAEDz/j1US0/4ESnqak155k5p\naqL74x8f9usfJtLP/RygJu7rwbifuYNIIP4lIhn0w0Qy8vGXDXt7vqLGEalbf5HIotjvEKlz/0Q/\nY/gqkcA+fh/KnwL/BZwNrAZSuS1G+IYbsLjdKTxC5tlstlhgU6ibCXWkaVOLKS0uTPXQUiLSI7qU\nka7UWGxtxZKiDV8Gouzdi3/16rQeM1f5v/xlzJ52un0vVPOVpmm91rXMLxn8PN2Gk9Ly3FycXlzg\npNNVPaIyOIBZLh9aihNqfanvvotfOkslJLRyJeF582KP7fbsWoeV9QG8p7yMirLI4oED+/czznIs\nwyPKXVW2MPqB1Gfu1J07CZ7ft2Fj4gwibR2NPl//1Ofn7gYOAN3ABk7cYGkDJ9apnwG8QaTkppFI\ne8j+/ILIxUG8OcC7RDaU+kFiv8qwGHV1qDNn5n3Wrm8burklwUHXuYRQsRR70HKsjUXBCHpE91Xi\nbYOuNJcTfvghgbgeyKJ/hseD/4tfjD222+15P48hMpfjg5sp7jCl+smz8CYKfmcFTnv2lCQkIlll\ncADTzVbUPbtHPqihCAQwx4+XWoZBmBYLvm98I/bYarVmVfYdciCAB5g7awZenw8TCB47gsMi/+sN\nxwLLEfTt29NyrHBVFWaWXa3misDXvoa1ri7Tw0iL+PIChwVmFyeSuXNTlkOZO01VcI2gR3S8Et1A\nP5TidnMDGGlp3Gjgv/126PkbqaqaVfWyqaZpWqw7h6LAgtLB53KHaaOovG9z3+wVKYOrplVJxjww\ncHS0gpHK1VT9M02zV123OFHw05/GOOX4CrxsXIieEwH8qVMm4ej54+1u3MFUq2Thh6PY2wZeb1qO\npRw9im/FirQcK5+YTicsXToqsnZwYuZudlEIxyBbshso+F0enPbcKE0YaY/oeEvsLWhbNiflvYbs\n4EH8n+iv4EwAhMeNI3DttbHHoyX7Hi9+Lo93hamyDb44/aheSmlxcuZHqpWXldCqlTHyhsUw3RlA\n2/HByAc1DMp77+H77GcH/8FRyrTb8f3f/xt7bLPZsi77DjkSwFssFiaNqycUCuHzB7B5m7EOcpIX\nvZVZw+gH05e5U7dvJ7ByZdqOly/8d96J9dRTMz2MtIpfGGRVYU4C9bMdppXC8qqsX9A60h7RfVV0\nt0FHR1Lea6iUnTsJyEX5gPxf/zr0ZNwtFkvW9IpOJ4vF0uuuw6LSIIM1ngiYFrTiqqxf0Op22gkU\nVBNKUtg0S2lGbWxMynsNmddLeNIkKaMZQOCGGzDHjAFOLPXMJjkRwAMsWzCHYChyNb9z+xZOtUkW\nfigWac1Yt6Wnb3SUUVODOYpuIY+U6XbDypV5t/PqYPpm4WcUhigYpIsFQKtWQllZ9raiS0aP6HhF\nmoF+5GR7DaeeIaVx/QrPnEnwsstij0dj9j0qfi5XOwzqBtmdFaAVF2UV2VtKo6kK7ooxdJKsQM7A\n2XEUwoPfoUgVw2IhlMedzobLLCrCf+utscfxzRayTc4E8AVuFxPqagmHw/j8fvSuI9gkC5+wMm8r\ndHam9ZhmRwf+885L6zFzme/OO7HOnJnpYWRE3y4WCxLIwodRMQqrsNuy7yIxWT2i4y12tKG9917S\n3m9YmpoIXHRRZseQZfq2mouvBR+NVFXt1XlnUWkAZZBcr4lCh6OC4oKRtVhNlWSWwQGc4gyiNe5I\n2vsNh7p5M/64ki8R4b/lll67rmZzF6mcCeABzl2+mEAw0mpu5/atnGo7muER5YYizUBvSn/mzrJt\nG/5Vq9J+3FxkFhairFyZlXV26dA3C39KQZhax+DZqWOmnZKKarItQeLxVIy4R3Rfld1tcCyzdx6V\n7dsJjKBFbD4Krl5N6NxzY4+zrdVcJsSXHJTZTGYXDd4i1mdqWMvGoGvZNZmTXQYHcLragmXnzqS9\n37B0dhKeOjWzY8gy4VNPxf/lL8ceZ3P2HXIsgC8qLGDSuDrC4TD+QACt8wh2ycIParGtBWuatl3v\nxTAwamsxR1lJyHD47roL6yi/nalpWq/62YaKANZB2kqCQqteiqe8LLWDG4LiAiedzqoR94iO59YM\nrC2Hk/Z+IyGlcccZHg++tWtjj61Wa17vupooVVV7BfHzS4IUD9JWEiKlNOWe6lQObUiSXQYXYeDu\nbINg5ve0MaxWwpMmZXoYWcHUNLwPPdRrHUu2d5HKqQAe4LwzlhAMRjJzO97fyky7ZOEHU+FtyVjm\nzvT5CJx5ZkaOnSvMoiKUFStGbfY9XnztcIFmsrgsMOhrQqZCt7uKAlfyylWGy6pb0Mtqk9IjOt4i\nx7HMl8/0MI4eHdE+D/nCBLr/5V96bdok2ffjbDZb7GJGU+HsisRKadpsFZSVZH7XX1WBIk9NUsvg\nAMY7wmgf7krqew6XumULvmuuyfQwsoL/1lsxZs2KPXY4HFmdfYccDODdLieTJ9QTCoUJBIIoxw7h\nsqS/j2qucGsG1uYjGTu+ZetWfHGLu8SJfP/8z1in992GanRSVRWH4/gJc3phYqU0XaYVR8UYdC1z\nH2mxHtEkv463xt8Kra1Jf9/hULduxS9zOlI6c/HFscdOpzPrT/jppChKr7lcaTcSKqUJmipGcQ2u\nDLeJ9VSU06qVJP1951maUdO8++qAjh0jNErXXcULn3oq/n/4h9hju92eE3fSci6ABzh3+SJCPau3\nP9i+ldNsLRkeUfZaaGtD35rebdd7CYUw6uow5cTWr9CUKaiXXCLZ9zi6rvdaBJhYKQ204Ka8qiZj\nrSXLy0tpSVKP6HgO1cDa3JTU9xwRw8AYM2ZUl8b1VzozmheuDsRisQyrlKbdtFNQOTZjOy4XuZ10\nuqoJm8k/fkFXGwQGv7OYLobLRbi+PtPDyJj+SmeyeeFqvJyMGlxOJ9MmTSAYDBEKh2nZs4OxVl+m\nh5WVqn0tKG1tGR2DGQoRXLIko2PIRibg//73sY4bl+mhZJ3425eJltKAQotWRqUn/bu0up12Au5q\nwin4SF3o7EDbkh3lM1FGZyfBs87K9DAywgS6v/99KZ1J0HBKaQCalQIqqtN/QW7VLFjLxiS9DA6g\nzh5E++ijpL/vSKjbto3qTZ1ysXQmKicDeIh0pIlmLfft38c4mrAkkKUbTZyqga0l85k7y+bNdK9Z\nk+lhZJ3ua67BtmxZznxYpNNwS2nCqHS4qtO6s+PxHtGpydrUBVtRmjI/j+OpW7bgH6VzOrh6NaG4\nVppSOnNywy2lMVFo0cvTekGuAGVV1bQqrpS8/3y9FUua92MZVEsLodNOy/QoMiJXS2eicjaAt9ms\nnLloPj6fH4D3N7/NLHt7hkeVXeY7jmW2fCYqGMQYP152fYtjFhXBV76CVpAbW4hnwnBLaXymhlFS\nS4EzPVnRZPeIjmdVDaxtzSl57xGJlsaNstIvKZ0Znv5KaUoSKKWJXpCXFRemcngx5WUltKagDC6q\nsKsNfNlXLWAUFmJUZ0/3n3QwdT1nS2eicvrTd/aMKZSVlmAYBl5vN+rRvRRpg1/ZjxZj/c0ozdlx\n8jdNk9DcuZkeRtboeuAB7LNnZ3oYWa9vKc05ngCDbc0O0GHasXvqUr7JUyp6RMeb7+hE27I5Je89\nUobfT3Dx4kwPI21MXcf79NNSOjNMfUtpzq/0J3xBHioeS6E7tZs8uZ02goXVhFIUFlXZQuj79qbk\nvUdK/eADuq++OtPDSCvf/ffnbOlMVE4H8IqicMn5ZxEIRPqp7ti+jVnWFhI5weerD9/4Kz/76ie4\n/4Lx1M0+lV+8/fYJP/OdDRuY9uCDVN97Lxc//TTbjwzepeal3bs589FHqbr3Xk774Q956vXXe31/\nQ2Mjc//1X6n7znf4/G9+QzBui+hOv58F117LW+ecM/JfMA8E5s3DetFFsnA1AX1Laca5wgnt0gqR\nntLFVXUp60yTmh7RvU0It6IcPJiy9x8J9b338F91VaaHkRYm0P3d7xKOW8sjpTND07eUptRqco7H\nn1A9fCdW9PKxKetMEymDq6XDtA3+w8O02NqKJRvuiPfnyBFCCxZkehRp4//c5whcd13sca6VzkTl\nfARRVlLMzOlTCASCGKbJvh1bOcXuzfSwMibg66Jq8kxuu+tbOHT9hLzgD156iYdeeYW1F17I+htv\npMLl4hP//u90+v0DvufutjYu//nPWVRXx1+/8AVuXbaMO55/nv/XszmUYRhc/9xzXDd/Pn+67jre\nPnCAp994I/b6e9ev59IZM5i8bNkovrSKMHWd4Pe+hz5mTKaHkjN0Xe91a3NuSYiJrsTutDUpBZTX\n1CW9m4WqQHHlmKT3iI6nYWDLxvKZqEAAY9y4UTGnA9dfTzCuX7bdbpfSmWGwWCx9LsiNhC/Ij+LE\nVVUfuXBOsorKSlpSVAYXVextA2/2xiZmcTFGRUWmh5FyoWXL8D3wQOxx3/NLLsn5AB7g7KULY/8B\nmpqaKOnaR4FldJbSTFl6Aed/6Ztcc9biE/7jmqbJw3/7G/9n+XJWTpvGNI+Hh1etotPv59cn2STm\nqddfp6awkAdWrGByeTlXz53LFaedxo83bQKgxeul1evl+vnzmerxsGLKFD7oWXT3xr59bNy1i384\n4wwMi4XwKO852/ntb2NfulQyd0PUN2A6qyJAuTWR/R8UmtRiKsbUYkliEO/xVNBiKU7a+/VnrrsL\nLdsWvPVhhEKE58/P9DBSKnTGGfjuvz/2OJdP+NnAarX2qoefUxJicoIX5C24Kayqw6YnL1taWlzA\nUbsnqTsn91VuDaMf3J+y908GpbER/5VXZnoYKWXU1+P96U971b3nYulMVF4E8Lqmcf6ZS+juWdC6\nbct7nG5tTujWXD4aKHO3p62NI52dnD1xYuw5u66zpL6eV/cOXJv32t69nBX3GoCzJ07krQMHCBsG\n5S4XVQUF/KWxEW8gwKY9ezi1qopQOMwtv/8937/4YnSLBW3LFryf+UzyftEc4zv7bKyf/jQWydwN\nmaIoOJ3OWNmRrsKKKj8Oy+Bz3ESh2VKGpyY5QXyR20mnsyolPaLjTQq3ouzP7pO+unkzvjwuowmP\nG4f36aehZ87m+gk/W9hsthMWqFfYBu8yBZH2kiU145ISxNttOkpxasvgABbb2rBsydLymagDBwgu\nXZrpUaSM6XbT9YtfYJaVAcfPKbk8l/MigAc4ZcI4TpkwjmAohGEY7Nz8FrMdo7MrzVxXJ/r72094\n/nBnJwAVrt4tsspdLo70fK8/TV1dePq8psLlImQYtHi9KIrCU5ddxndfeIHFDz3E7JoarjztNP51\n0ybm1dZS7nKx4sknmXv//dz31ltJ+A1zj1FcTPC++7DW1GR6KDkr+oEb5dZMLqj0oyZwoW6g0KyN\nPIhPZY/oeCoGjmOtYGZ5EsLnIzx5cl6mSsyCAry//GWvRau5fsLPFn0vyDUVLqgM4EzgghwUmpIQ\nxKsKFHtqOJqCnZP7Kutug46OlB9npIzSUsLFqb2zmAmmouB95BGMGTNiz8X//5ercnv0fVx4zhlY\ndR3TNGnv6MA4spNq68C13flqYqAZdYiZu5GelBbV1bH+xht556tf5bsXXshHR5/K6a0AACAASURB\nVI/yszff5J/PO48bnnuOK08/nRc+/3l+8/LL/H6UBbEm0P7ww7gXLEjqyV9V1RO+fvKTnyTt/bOR\nxWLpFcRX2Q3OqEisM00siB8zvB0eU90jOt5pLh+W7dtSfpxkMBQl70rjTEXB+5OfYEybFnsuH074\n2aTvBVH0gjyx/VwiQXxxzbhhd5qqqCinVSsZ1muHokQz0A9n50L0vpQ9e/Jyfwf/nXcSuvji2GOH\nw5EXa1jy6tNI1zQ+ccG5+PyRXRt3NTYy0TiIXc3H/FD/NAzs7a39fq/S7QYiGfV4TV1deHq+1x+P\n231Chr6pqwtNVSlz9p+9+Op//zf/fN55KMA7Bw9y6amn4rbZWDFxIn8eP34Iv1Hu67j1VlwXX5yS\nzN3jjz/OoUOHYl9Xj4JWYLqu92rfN60gzGkJbAwDkSC+yVJKeW09Vm1o2btU94iON81sRs2yHRsH\nom7Zknc7OfruvpvQihWxx/lyws82fRe1VtoNGsoTuyAHhWalkMLqcUPuTlPkduJNQxkcwGJHG9rm\n7NpJeUAffUSwoSHTo0iqwKWX4r/jjthjq9WaN2tY8iqAB6ip8rB47mmxIH7L228y3940aurhZ7m8\naP2UzwDUl5RQ6XazvrEx9pwvGORvH33EwrFjB3zPBbW1bNi1q9dzGxobmTNmDJZ+MlLPvPUWbquV\nj0+fjtFTAhBtK+nv7iZUkvqsR7bwL1iAdtNN6Ce5QIpat24dhYWFGEZkcebOnTtRVZWbbrop9jN3\n3XUX5513XuxxUVERHo8n9jVa+lJbrVZ0/XjmbXFZkBkFiXWzMHsWtpaOGZdwR4tU94iOp2JgP9YG\nRiKLdLNARwfhqVMzPYqk8d1+O4GvfjX2OJ9O+Nmo7wX5KQVhzigPkmg76Bbc2KvGU+hOrCOUrqnY\nysfgJbV7RER5fG1w7FhajpUMZkUFZp5sMBi8+GK6H3009ljTtLw6R+ZdAA+wdP7pVHsqCIXCBEMh\nGje/yVzn0UwPKy3Gtu9hy2uv8e7Bgximyd5jx3j34EH2HTuGoijctGgRP3jpJX6/bRtbDx/mi//1\nX7itVlbH3QL//G9+wxd++9vY42vnzeNgezt3rlvH+01N/OyNN/jlO+9wc1xP5Kimzk7WvvACD/Zs\nNV7scDDN4+FHmzbxzsGD/H7rVhaedhpGfX3q/xgZFq6tpfuHP8TRZwHwQJYtW4bP5+P1nh77Gzdu\npLy8nI0bN8Z+ZuPGjZx11lmxx7fccgsVFRUsWLCARx99FDPba6aTJNpTOr537xkVQaa4E+0+pdCk\nFOKuHk+B8+Qn/nT0iI43w+VH+6D/i/BsZVithCdPzvQwRsx/883477or9jjfTvjZqu9F0ozCEEvL\nEg/ij+JErRif0I6t5ZU1tJD6MjiAAs1Abz6clmMlzYED+C+9NNOjGLHgeefhffLJ2AJ0VVXzbg1L\nXgbwiqJw6UXnoahKpB6+vZ2ju95juj37F5GMhIrBtlc3ceYjj3Dmo4/iC4X4zoYNnPnoo3xnwwYA\nblm2jC8uXsw//OEPnP3YYxzp6uI3V12FK+7Dc397O/vjMgb1JSX86sor2bRnD2c88gj/8tJLrF2x\ngpVx9aFRd65bx5eXLKG68PgH6UOrVvGH7du55Kc/5ZLp01lVUUFXnpd6mAUFtD76KEULFyb8geF2\nu5k7dy7r168HIsH6zTffzJ49ezh8+DBer5fXX3+dhp5bnN/85jf51a9+xV/+8hfWrFnDbbfdxn33\n3ZeqXynrKIqCy+XqFcSfVRFIuCUdRDZ7slSe/MRfUVlJc4p7RMc7lRbU3bvTdrxkUDdvxnfttZke\nxoj4b7gB3733xh5rmpZ3J/xsFd3VNv6u2qyiEAtLEw/iO0wbvpJ6KivKBixyqygroVVPTxkcwGLH\nMbSTtGjORkpjI4Hzz8/0MEYkdMYZeP/936EnrlFVFZfLlXdzWTHzOGV34PARfvHbP2C3Rf4jTpgw\nibby6ewN5GdGZabTy+JXf4flww8zPZRBGTNmUHLZZZkeRkqYFgtNTz9N2RVXDHl3tzvvvJO33nqL\ndevWUVdXx3PPPccdd9zB5z//ecrLy1m1ahVHjx7ttx73e9/7Hvfeey9Hj46Ou01RpmnS2dkZKz0y\nTFjfZGVHZ+I1yzbCFHYf5PCRpl6NX0qLC+guGZ/yNnPHGVwX3oz1D/+dpuMl0Zw5FF1ySaZHMSz+\nG27A993vxh5bLJa8POFnO9M06e7uJhg8Xg73ZpvGq206iQbdFgzKAk00HTpIyDg+md0OG1rVxLTd\nSQP4rLUR56+fTdvxksVctozCSy5BzeKNpwYSbGjA+4tfQM/6PEVRcLvdebkAPf9+ozg1lR5WnLU8\n1h9+166d1HTvoVRLrFY210wPN2HZsyfTw0iIWViIkYfdaEygde1aSj75yWFtzdzQ0MDLL7/M9u3b\naW9vZ+7cuTQ0NLBhwwZeeOEFlixZMuBiuvnz59Pe3k5TzyZao0U0Ex/9gFYVOKciwNSCxDPxfiy0\nOsdQNWZsbHFrunpEx5vmDGJp3Jm24yWT6XIRHjcu08MYMv/NN0vwniWipXHxn3FzSoZWThNGpcnq\nobx2PM6exa0WVcHtGZPW4N2pGugtR9J2vKRqaiKQgxfjwY99DO9//MeoCN4hzwN4gBlTJrFozuxY\nEL/lvXeYoRxIsN9sLjFwdLTmzMI3y86dePNwA5hjt9yC48or0QfozjOYpUuX4vf7Wbt2LcuXL0dV\nVRoaGli/fj0bN26Mlc/05+2338bhcFCch318BxO9RRr9oFaUSDlNogtbAcKmwhGtjOLaCRQXOClJ\nU4/oeLOVZiw7czOAV7ZuzbkyGt/tt/cqm5HgPfOi7SXjg/hZRaEhLWyNLFQvwl41gbLiQjyVlbQo\ng9fHJ9MiV3vOlc9EKe+/T7BnHVuuCK5cGSmb6Vmz0jexk4/y9zeLs2zBHKZMHIfPHzmZv/vm35lv\nPYSWUL/Z3DDN4ceaS5m7piZCc+ZkehRJ1bVqFcpNN+GsrBz2e0Tr4J955pnYYtWFCxeyb98+/va3\nv8UC+N///vc89thjbN68mcbGRh5//HHuvvtubrzxxl51pKNJ3yAeIgtbTytK/MQPCi24CZVPpCUN\nPaJ7M3B2tkEo8TsHWaWlhdCsWZkeRUJMRaH7nnt6LViV4D179BfEzygMcXZFIKGN26KO4cBbMo42\nuwcjTXXvUbWBNpTmE3dEzxVGdTVmjnRfClxxBd6nnorVvEcz78O5C55LRkUArygKF5/bQGV5KcFQ\niHDYYMubr7LU0ZQ3QfxMswlLn1aP2c4oLsbweDI9jKTouugiur/+dYqmTBnxezU0NBAOh2PBut1u\nZ9GiRdjtdhYsWABEujY8/PDDLFmyhNmzZ/OjH/2Ib33rWzz44IMjPn4uU1X1hA/uxWVBzq4IJLhB\nTES3qaWlR3S8CY4Q+q7GwX8wi5kFBRhjxmR6GCdlut14n3mmV6tITdMkeM8y0SA+PiExpSDMJTV+\nHEO4gx4wLQTTWAYHYFMNbG25G7wDmG1tBC64INPDOClTVen+5jfpfvjhXt1m8rlsJl5eL2LtKxAI\n8sR/PEcwGERVVew2KzPmLuHl7gpCaT5ZJ5fBNcF3cfzx+UwPZGhqatC2bMH1wx9meiQj0nXRRbTd\ncQe1Z5yR6aGIHqZp0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NNx/HZRWVkZdROnsl8pZZffznAC+XNcLUxe9yuUtrYkjzr7GPPn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\nCCGEECKHSAAvhBBCCCFEDvn/P7XmSRjSiX4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabeee66c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plotpie(ax, start, width):\n",
    "    bars = ax.bar(thetas, radiis, width=[s/100.0*2*pi for s in sizes], bottom=0.0\n",
    "             #,labels=labels, colors=colors\n",
    "             )\n",
    "    betabar = ax.bar(start, 0.8, width=width, bottom=0.1\n",
    "             #,labels=labels, colors=colors\n",
    "             )\n",
    "\n",
    "    bars.set_label(labels)\n",
    "    for label, c, bar in zip(labels, colors, bars):\n",
    "        bar.set_facecolor(c)\n",
    "        bar.set_label(label)\n",
    "        #bar.set_alpha(0.5)\n",
    "    betabar[0].set_facecolor('gray')\n",
    "    betabar[0].set_alpha(0.4)\n",
    "    \n",
    "    ax.set_xticks(thetas)\n",
    "    ax.set_xticklabels([theta/100.0 for theta in cumsizes])\n",
    "\n",
    "\n",
    "labels = 'w1', 'w2', 'w3', 'w4', 'w5'\n",
    "sizes = [10, 20, 40, 10, 20]\n",
    "cumsizes = [0, 10, 30, 70, 80]\n",
    "thetas = [theta/100.0*2*pi for theta in cumsizes]\n",
    "radiis = np.ones(len(sizes))\n",
    "colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', 'red']\n",
    "\n",
    "explode = (0, 0.1, 0, 0, 0) # only \"explode\" the 2nd slice (i.e. 'w2')\n",
    "ax0 = plt.subplot(131)#, polar=True)\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True)#, startangle=90)\n",
    "plt.axis('equal')\n",
    "\n",
    "\n",
    "#fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(8, 4))\n",
    "ax1 = plt.subplot(132, polar=True)\n",
    "plt.yticks([])\n",
    "ax1.set_title(r'index$\\rightarrow$w2,beta=55%')\n",
    "plotpie(ax1, 0.1*2*pi, 0.55*2*pi)\n",
    "\n",
    "ax2 = plt.subplot(133, polar=True)\n",
    "plt.yticks([])\n",
    "ax2.set_title(r'index$\\rightarrow$w3,beta=35%')\n",
    "plotpie(ax2, 0.3*2*pi, 0.35*2*pi)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Represent all of the particles and importance weights in a big wheel.\n",
    "    * Each particle occupies a slice that corresponds to its' importance weight.\n",
    "    * Particles with a bigger weight occupy more space, where particles with a smaller weight occupy less space.\n",
    "* Initially, guess a particle index, uniformly from the set of all indices. Suppose that is $w_2$ indicated in the first wheel.\n",
    "* Construct a variable, for example $\\beta$, initialized to zero, and added a uniformly drawn continuous value between zero and $2*w_{max}$, which is the largest importance weight.\n",
    "* Start at the position $w_2$, and add a beta that brings you to $w_3$, as shown in the second wheel.\n",
    "* Now iterate the following loop; if the importance weights of the present particle $w_2$ doesn't suffice to reach all the way to , then subtract from the index and increase the index by increments of one. What you have done is to remove a section of w[index] (now $w_2$) and to move the index to the next w (here $w_3$), as shown in the third figure.\n",
    "* By repeating this, eventually get to the point where beta is smaller than w[index] (here $w_3$ as shown in the third figure), then pick the particle associated with that index.\n",
    "* Do this N times to get N particles, and we can see that particles will be chosen in proportion to their circumference on the circle.\n",
    "\n",
    "This is written as:\n",
    "\n",
    "```python\n",
    "        p3 = [] \n",
    "        index = int(random.random() * N) \n",
    "        beta = 0.0 \n",
    "        mw = max(w) \n",
    "        for i in range(N):\n",
    "           beta += random.random() * 2.0 * mw\n",
    "           while beta > w[index]:\n",
    "               beta -= w[index]\n",
    "               index = (index + 1) % N\n",
    "           p3.append(p[index])\n",
    "        p = p3 \n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: New Particle Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Take the list of particles and importance weights to sample N times, with replacement, N new particles with the sampling probability proportional to the importance weights. \n",
    "    * Construct a new particle set p3 = [], so that the particles in p3 are drawn from p according to the importance weights, w."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "    p2 = []\n",
    "    w = []\n",
    "    for i in range(N):\n",
    "        p2.append(p[i].move(0.1, 5.0))\n",
    "        w.append(p[i].measurement_prob(Z))\n",
    "    p = p2\n",
    "\n",
    "    p3 = []\n",
    "    # Write code here so that the particles in p3 are drawn from p\n",
    "    # according to the importance weights, w\n",
    "    \n",
    "    p = p3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Programing the particle filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Program the particle filter to run twice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xafb6394c>]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OSULgGDakQZ8lKU2arGADtqgy6FYyi7hLXNjP7RSgN1rUKfp+aS4OF/Y7KNICyTcUoAfA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xae89454c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 1000 \n",
    "T = 2\n",
    "\n",
    "myrobot = robot()\n",
    "\n",
    "p = [] \n",
    "for i in range(N):\n",
    "    r = robot() \n",
    "    #r.set_noise(0.01,0.01,1.0) \n",
    "    r.set_noise(0.05, 0.05, 5.0) \n",
    "    p.append(r)\n",
    "\n",
    "for t in range(T):\n",
    "    myrobot= myrobot.move(0.1, 5.0) \n",
    "    Z = myrobot.sense() \n",
    "\n",
    "    p2 = [] \n",
    "    for i in range(N):\n",
    "        p2.append(p[i].move(0.1, 5.0))\n",
    "    p = p2 \n",
    "\n",
    "    w = [] \n",
    "    for i in range(N):\n",
    "        w.append(p[i].measurement_prob(Z))\n",
    "\n",
    "    p3 = [] \n",
    "    index = int(random.random() * N) \n",
    "    beta = 0.0 \n",
    "    mw = max(w) \n",
    "    for i in range(N):\n",
    "       beta += random.random() * 2.0 * mw\n",
    "       while beta > w[index]:\n",
    "           beta -= w[index]\n",
    "           index = (index + 1) % N\n",
    "       p3.append(p[index])\n",
    "    p = p3 \n",
    "\n",
    "#plt.plot([p[i].orientation*180.0/pi for i in range(100)])\n",
    "#for i in range(N):\n",
    "#    print '#', i, p[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if you move ten steps forward?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[x=89.510 y=97.261 orient=0.2782]\n",
      "89.1827076158 97.013774268\n"
     ]
    },
    {
     "data": {
      "image/png": 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/oaXHwFOJWS5DbgSKaSTDQN38GJIvAol+FFczUklBixwHkoyiaUjxIczuzcgS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CoVXUfKKvqmRjAcDiuSpt/va5SsZVnKrvT7/fQ31rBSCSzDwMLg+vycdr2BGY0wODxi+fIK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\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xUbSzgqxzHIeDgwMePXqk90vZNkwmExaLhUpI3kgIXqP6CZ/ASok0qNPp8M1vfpNf+7Vf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xafc079ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RfBNlIkuY8Fpqvc6zVglPfmA3+Tjcn+bXZ0vCTS2FilWYAAv3CHv/2iVksQoB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\n8PlfGEGSLLsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac95d30c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "\n",
    "#cm = plt.get_cmap('gist_rainbow')\n",
    "cm = plt.cm.jet\n",
    "\n",
    "N = 10000 \n",
    "T = 30\n",
    "\n",
    "myrobot = robot()\n",
    "\n",
    "p = [] \n",
    "for i in range(N):\n",
    "    r = robot() \n",
    "    #r.set_noise(0.01,0.01,1.0) \n",
    "    r.set_noise(0.05, 0.05, 5.0) \n",
    "    p.append(r)\n",
    "\n",
    "xs = [pp.x for pp in p]\n",
    "ys = [pp.y for pp in p]\n",
    "c = plt.scatter(xs, ys, c='gray')\n",
    "c.set_alpha(0.5)\n",
    "\n",
    "lmc=plt.scatter([r[0] for r in landmarks], [r[1] for r in landmarks], marker='v', c='g', s=100)\n",
    "mrc=plt.scatter(myrobot.x, myrobot.y, c='k', marker='s', s=75)\n",
    "\n",
    "for t in range(T):\n",
    "    myrobot= myrobot.move(0.1, 5.0) \n",
    "    Z = myrobot.sense() \n",
    "\n",
    "    p2 = [] \n",
    "    for i in range(N):\n",
    "        p2.append(p[i].move(0.1, 5.0))\n",
    "    p = p2 \n",
    "\n",
    "    w = [] \n",
    "    for i in range(N):\n",
    "        w.append(p[i].measurement_prob(Z))\n",
    "\n",
    "    p3 = [] \n",
    "    index = int(random.random() * N) \n",
    "    beta = 0.0 \n",
    "    mw = max(w) \n",
    "    for i in range(N):\n",
    "       beta += random.random() * 2.0 * mw\n",
    "       while beta > w[index]:\n",
    "           beta -= w[index]\n",
    "           index = (index + 1) % N\n",
    "       p3.append(p[index])\n",
    "    p = p3 \n",
    "\n",
    "    xs = [pp.x for pp in p]\n",
    "    ys = [pp.y for pp in p]\n",
    "    xs_m = float(sum(xs))/len(xs)\n",
    "    ys_m = float(sum(ys))/len(ys)\n",
    "        \n",
    "    #currentcolor = colors.rgb2hex(cm((t+1.0)/T))\n",
    "    currentcolor = cm((t+1.0)/T)\n",
    "    plt.scatter(xs, ys, c=currentcolor, alpha=0.3)\n",
    "    plt.scatter(myrobot.x, myrobot.y, c='k', marker='s', s=75)\n",
    "    pfc=plt.scatter(xs_m, ys_m, c=currentcolor, marker='d', s=100)\n",
    "\n",
    "#plt.axis('equal')\n",
    "plt.legend([lmc, mrc, pfc], ['Landmarks', 'True', 'PF'], loc='best')\n",
    "\n",
    "# show colorbar\n",
    "pfc.set_array(np.arange(T))\n",
    "cb = plt.colorbar(pfc, shrink=0.8, aspect=7, cmap=cm)\n",
    "# showing colorbar will change the color of the last estimate, so redraw it\n",
    "plt.scatter(xs_m, ys_m, c=currentcolor, marker='d', s=100)\n",
    "\n",
    "# show PDF (2D histogram)\n",
    "H, xedges, yedges = np.histogram2d(xs, ys)\n",
    "fig = plt.figure()\n",
    "#im = plt.imshow(H, interpolation='nearest', origin='low',\n",
    "#               extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])\n",
    "#fig.colorbar(im)#, shrink=0.5, aspect=5)  \n",
    "#plt.axis('equal')\n",
    "ax = fig.gca(projection='3d')\n",
    "xcenters = xedges[:-1] + 0.5 * (xedges[1:] - xedges[:-1])\n",
    "ycenters = yedges[:-1] + 0.5 * (yedges[1:] - yedges[:-1])\n",
    "X,Y = np.meshgrid(xcenters, ycenters)\n",
    "surf = ax.plot_surface(X, Y, H, rstride=1, cstride=1, cmap=plt.cm.coolwarm, \n",
    "                       alpha=0.3, linewidth=0, antialiased=False)\n",
    "fig.colorbar(surf, shrink=0.5, aspect=7)\n",
    "\n",
    "print myrobot\n",
    "print xs_m, ys_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Rather than print out the particles themselves, you can print out the overall quality of the solution. \n",
    "* To do this you already have an eval code that takes in the robot position, r, and a particle set, p, to compute the average error of each particle, relative to the robot position in x and y, not orientation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def eval(r, p):\n",
    "    sum = 0.0;\n",
    "    for i in range(len(p)): # calculate mean error\n",
    "        # the last part of the line is normalization for a cyclical world\n",
    "        dx = (p[i].x - r.x + (world_size/2.0)) % world_size - (world_size/2.0) \n",
    "        dy = (p[i].y - r.y + (world_size/2.0)) % world_size - (world_size/2.0)\n",
    "        err = sqrt(dx * dx + dy * dy)\n",
    "        sum += err\n",
    "    return sum / float(len(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take the function eval and produce a sequence of performance evaluations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.60349245739\n",
      "4.08533592302\n",
      "3.53423501432\n",
      "3.31781191877\n",
      "2.16083931165\n",
      "1.42428895437\n",
      "1.66998736603\n",
      "2.04813066851\n",
      "2.30006444212\n",
      "2.39219418552\n"
     ]
    }
   ],
   "source": [
    "\n",
    "N = 1000 \n",
    "T = 10\n",
    "\n",
    "myrobot = robot()\n",
    "\n",
    "p = [] \n",
    "for i in range(N):\n",
    "    r = robot() \n",
    "    #r.set_noise(0.01,0.01,1.0) \n",
    "    r.set_noise(0.05, 0.05, 5.0) \n",
    "    p.append(r)\n",
    "\n",
    "for t in range(T):\n",
    "    myrobot= myrobot.move(0.1, 5.0) \n",
    "    Z = myrobot.sense() \n",
    "\n",
    "    p2 = [] \n",
    "    for i in range(N):\n",
    "        p2.append(p[i].move(0.1, 5.0))\n",
    "    p = p2 \n",
    "\n",
    "    w = [] \n",
    "    for i in range(N):\n",
    "        w.append(p[i].measurement_prob(Z))\n",
    "\n",
    "    p3 = [] \n",
    "    index = int(random.random() * N) \n",
    "    beta = 0.0 \n",
    "    mw = max(w) \n",
    "    for i in range(N):\n",
    "       beta += random.random() * 2.0 * mw\n",
    "       while beta > w[index]:\n",
    "           beta -= w[index]\n",
    "           index = (index + 1) % N\n",
    "       p3.append(p[index])\n",
    "    p = p3 \n",
    "\n",
    "    print eval(myrobot, p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What You Programmed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* You just programmed a full particle filter.\n",
    "* With a very primitive robot simulator that uses landmarks as a way of taking measurements and uses 2-dimensional robot coordinates, you solved the estimation problem in just **30** lines of code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Math Behind It All"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iteration of Measurement + Motion (Prediction):\n",
    "\n",
    "* **Measurement** update: Production followed by normalization\n",
    "    * Compute posterior over state, given a measurement update, that was proportional to the normalization of the probability of the measurement, given the state, multiplied by P(x)\n",
    "\n",
    "  Bayes's rule\n",
    "\n",
    "  $P(x|Z)=\\frac{P(Z|x)P(x)}{P(Z)}$\n",
    "\n",
    "  x=state (particle)\n",
    "\n",
    "  Z=measurement\n",
    "\n",
    "  $P(x|Z)=$ probability of state after measurment\n",
    "\n",
    "  $P(x)=$ probability of state before measurment, represented by the set of particles\n",
    "\n",
    "  $P(Z|x)=$ probability of measurment on the condition of state, importance weight \n",
    "\n",
    "  $P(Z)=\\sum P(Z|x)P(x)$ total probability of making measurement \"Z\"\n",
    "\n",
    "    * By resampling transfer the importance weight back into the set of particles so that the resulting particles $P(x|Z)$ will represent the correct posterior.\n",
    "\n",
    "\n",
    "* **Motion** update: Convolution\n",
    "    * Compute a posterior over distribution one time step later, and that is the convolution of the transition probability, multiplied by the prior. \n",
    "  \n",
    "  Theorem of Total Probability\n",
    "  \n",
    "  $P(x^{t})=\\sum{P(x^{t-1})P(x^{t}|x^{t-1})}$\n",
    "  \n",
    "  $P(x^{t}|x^{t-1})=$probability of motion from state $x^{t-1}$ to state $x^{t}$\n",
    "  \n",
    "    * The set of particles is written as, $P(x^{t-1})$, and your sample from the sum, $\\sum$. By taking a random particle from $P(x^{t-1})$ and applying the motion model with the noise model to generate a random particle $x^{t}$. As a result you get a new particle set, $P(x^{t})$ that is the correct distribution after the robot motion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtaining an estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The weighted mean\n",
    "    $$P_{est}=\\sum^N_{i=1}{w_ix_i}$$\n",
    "1. The best particle\n",
    "\n",
    "    $P_i$ such that $w_i=max(w_k): k=1,\\ldots,N$ \n",
    "\n",
    "1. The weighted mean in a small window arround the best particle (also called robust mean)\n",
    "\n",
    "Each method has its advantages and disadvantages: \n",
    "* The weighted mean fails when faced with multi-modal distributions, while the best particle introduces a discretization error.\n",
    "* The best method is the robust mean but it is also the most computationally expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timing of Resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Resampling is used to solve the depletation of the population after a few iteration\n",
    "    * Most of the particles have drifted far enough for their weight to become too small to contribute to the *PDF* of the moving robot.\n",
    "* Measures that estimate the number of *near-zero-weight* particles\n",
    "    * Coefficient of variation $cv_t^2$\n",
    "    $$cv_t^2 = \\frac{var(w_t(i))}{E^2(w_t(i))} = \\frac{1}{N} \\sum_{i=1}^N{(Nw(i)-1)^2}$$\n",
    "    * Effective sample size $ESS_t$\n",
    "    $$ESS_t = \\frac{N}{1+cv_t}$$\n",
    "* When $cv_t$ or $ESS_t$ drops below a certain threshold, usually a percentage of the number of particles N, then the particle population is resampled, eliminating (probabilistically) the ones with small weights and duplicating the ones with higher weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variations on Resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Using a function of weights\n",
    "    * Instead of using directly the weights $w_i$ of the particles, a function of weights can be used, ex. the square root $\\alpha_i = f(w_i) = \\sqrt{w_i}$\n",
    "    * Then the new weight $alpha_i$ are normalized so they sum up to the number of particles N\n",
    "    * Then each particle is examined separately\n",
    "        * if its weight is greater or equal to one, k copies of it are propagated forward ($k = \\lfloor \\alpha_i \\rfloor$)\n",
    "        * otherwise, the particle survives with probability equal to $a_i$\n",
    "* Corrective resampling\n",
    "    * The particle population is \"injected\" during the update phase with a small number of particles created directly from the sensor data independantly of the rest of the particles\n",
    "* Maintaining the variance of the distribution\n",
    "    * During the resampling stage a small number of particles selected uniformly from the population are propagated forward given a small weight.\n",
    "    * The intuition behind this approach is to maintain the coverage of the predictive model in the particle population without affecting the accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Particle filter example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Assume the following Non-Linear System Model for the dependency of x and y:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "x &= 0.5 x + 25x / (1 + x^2) + 8 cos(1.2(k-1))\\\\\n",
    "y &= x^2 / 20\n",
    "\\end{aligned}\n",
    "$$\n",
    "    * x holds the actual state the world is in (the actual value we try to estimate)\n",
    "    * y holds the observed variable \n",
    "                        \n",
    "This is a well-known non-linear dependency, it is used for benchmarking novel, non-linear filters, see: \n",
    "> NJ Gordon, DJ Salmond, AFM Smith. *Novel approach to nonlinear/non-Gaussian Bayesian state estimation*, Radar and Signal Processing, IEE Proceedings F, 1993 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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D2vW+LVu2WD0UOIDPMT5Y8Tl+cOQD03GCN8F03/SEdJ2oOyFJqvHWaO2mteqc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\npOPHJW/jBk5KTZWSk8O/L20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TA2iwZcsWp4cAC/A5xoemn+P+k/tlqLF1rEtyF73/7vtO\nDMtn34l9puOSYyX8t9eCaP++9Fi/Xj2aHB9IT9eXTo65vl5D3W656utPHZ84oXfefFNGgvN/XUb7\nZ4ng8VnGvgEDBoT83jYr3926dZPH41FJSYnpfElJifLy8kJ+MIDYUFJl/r2fm5LbwpWRk5mYaTou\nry13aCQIV/I+8w9S1b16OTSS/3K75U1PN5+qqHBoMADiUZs/yiclJWno0KFavXq1JkyY4Du/Zs0a\nXXfddZYNpKCgwLJ7IfIafornc4xtgT7Hzz74zHTNGT3OcPxz9tZ7pTWNxxV1FTr/gvPlcXucG1SU\niZnfk4fNLUN9L7tMfZ0ec5cu0vHjvsPz+/VzdAWWmPks0SY+y/hxLIyWtKBWO7nnnnu0ZMkS/eEP\nf9Ann3yiu+66S8XFxZo+fbok6b777tNll11mes/HH3+sbdu2qbS0VBUVFXrvvfe0bdu2kAcKwBnR\nNtlSkjxuj7KSzb25R6uYGBeToq3nW/KfdEnfNwALBdXENnHiRJWVlWn+/Pk6cOCAhgwZohUrVig/\n/9RfwsXFxdq5c6fpPV//+te1e/duSZLL5dL5558vl8slr9dr8ZcAwE7RtsZ3gy6pXXSsujEUHa48\nrK5pXR0cEdrt8GHzaiIpKVKPHi1fHylMugRgo6BnkMyYMUMzZswI+NrixYv9zu3atSv0UQGIGnuO\n7TEdO73Gd4POqZ2162jjnzOs9R2Dmle9v/IVyR30xsv2YblBADaKgj/lAESzaK58N8Va3zEoGltO\nJCrfAGxF+AbQKr+e72ipfLPWd+yL1vBN5RuAjQjfAFp0svakyirLfMcel0d5naJjidHm4Zu2kxgU\nreGbyjcAGxG+AbRoX7l5DeYeGT2iZjm/5m0nVL5j0I4d5uNoDd9UvgFYiPANoEXR2nIinZpw2RQ9\n3zGoeeXbwbW0TZq3nZSVBb4OAEJA+AbQomidbCkFmHBJ20lsKS+XDh5sPE5IkHr3dm48TTUfx9tv\nOzMOAHGJ8A2gRdG4wU4DJlzGuOYtJ/36nQrg0eCiiyRPk/aq7dulAwecGw+AuEL4BtAiv8p3NLed\nUPmOLdHa7y1JGRlS8+2/X3/dmbEAiDuEbwAtiqm2E3q+Y0u0rnTSYMwY8zHhG4BFCN8AWhTVEy5p\nO4lt0TrZskHz8P3aa06MAkAcInwDaFE0V75pO4lx0V75HjnS3Pf96adScbFz4wEQNwjfAAIqry5X\neXW57zjZk6zT0k9zcERmGUkZ8rgaw9HJ2pOqrqt2cERol2ju+Zbo+wZgG8I3gICat5z0yuwltyt6\n/shwuVxUv2NVZaW0r8kGTm631LevY8Np0cUXm49pPQFggej5mxRAVInmlU4aMOkyRu3caT7Oz5eS\nk50ZS2vo+wZgA8I3gICieY3vBky6jFHR3u/dgL5vADYgfAMIKJonWzag7SRGxUr4zsyUhg41n6Pv\nG0CYCN8AAorFthMq3zEi2idbNkXrCQCLEb4BBBSLbSf0fMeIWKl8S2y2A8ByhG8AAcVi5Zu2kxgR\n7RvsNNW87/uTT6SSEufGAyDmEb4B+DEMIyYr37SdxICaGmn3bvO5r3zFmbEEg75vABYjfAPwc7jy\nsCrrKn3H6Ynpyk7JdnBEgTHhMgbt3i3V1zce9+ghpac7N55g0PcNwEKEbwB+ArWcuFwuh0bTMtb5\njkGx1O/dgM12AFiI8A3ATyy0nEi0ncSkWAzfo0ad2oWzAX3fAMJA+AbgJxbW+JZoO4lJsTTZsgF9\n3wAsRPgG4Mev8h2FK51IrPMdk2Kx8i3R9w3AMoRvAH5ipvIdYJ1vwzAcGg2CEksb7DRF+AZgEcI3\nAD+xsMa3JKUmpiolIcV3XFtfq5O1Jx0cEVrl9Uo7d5rPxULbiUTfNwDLEL4B+ImVCZcSky5jyt69\nUm1t43G3blJWlnPjaY9Afd/r1jkzFgAxjfANwKTeqNe+8n2mc9Fa+ZaYdBlTYrXfuwGtJwAsQPgG\nYHK4+rBq6xurk9kp2eqU1MnBEbWOSZcxJFb7vRsQvgFYgPANwKSkytzHGs0tJ5J/28n+4/sdGgna\nFOuV7+Z93x9/LB086Nx4AMQkwjcAk5LKZuE7iltOJOkrnb9iOn7mw2ccGgnaFOvhOzNTuuAC8znW\n+wbQToRvACbFVcWm42ivfN8w5AbT8YrPV/hNGEWUiMUNdpqj9QRAmAjfAEz8Kt9RHr6H9Rimc3PP\n9R3XG/X647t/dHBECMgwYr/nWyJ8Awgb4RuAycEqcw9rtLeduFwu3T70dtO5J999Ut56r0MjQkAH\nDkiVlY3HWVlS167OjSdU9H0DCBPhG4BJrFW+JenGITcqLTHNd7yvfJ9e+eIVB0cEP4H6vV0uZ8YS\njqws+r4BhIXwDcCk+WonvbN6OzSS4GWlZOn6s643nXvinSccGg0CivXJlk01bz0hfANoB8I3AJ+6\n+gQIiWwAAA/VSURBVDqVVpWazvXK7OXQaNqneevJy5+/7LdZEBwUD5MtG9D3DSAMhG8APqXVpapX\nve84Jz1HyQnJDo4oeMN7Dtc5uef4jpl4GWXiYbJlg+Z93x99RN83gKARvgH4xGK/dwOXy6XbL2g2\n8XIrEy+jRjy1nWRlSeefbz63bp0zYwEQcwjfAHz8dreM8pVOmrvxnBuVmpDqO95bvlcrv1jp4Igg\n6dQyg/EUviVaTwCEjPANwCeWK9+SlJ2SrevPbjbxcisTLx1XWiqVlzcep6VJ3bs7Nx4rEL4BhIjw\nDcDHr/IdY+Fb8p94+c/P/qkvy790aDSQ5N/v3b9/bC4z2BR93wBCRPgG4ONX+Y6xthNJurDnhRqS\nM8R3zMTLKBBvLSeSlJ1N3zeAkBC+AfjEQ+U70I6Xv9/6eyZeOikew7dE6wmAkBC+AfjEQ+Vbkm46\n5ya/iZerdqxycEQdHOEbAHwI3wAkSdV11Tpcc9h37Ha51SOjh4MjCl12SrYmnT3JdI4dLx0UTxvs\nNBWo7/vQIefGAyAmEL4BSJLfbpB5nfKU4E5waDTha77mNxMvHRRPG+w0Rd83gBAQvgFIOtWa0VSs\ntpw0GNFrhM7OOdt37DW8WrxtsYMj6qCOHj211GCDpCSpVy/nxmM1Wk8AtBPhG4Akae+xZuE7Bidb\nNhVox0smXjqgedX7K1+RPB5nxmKHiy82H//rX1J9vTNjARATCN8AZBiGNu3dZDoX6+FbkiafO1kp\nCSm+4z3H9mj1jtUOjqgDitfJlg1GjzavWf7JJ9KNN0pVVc6NCUBUI3wDHdyJmhO66fmb9Pg7j5vO\n987q7dCIrJOdkq1JZzWbeMmOl5EVaIOdeJKd7d968uyz0qWXMvkSQECEb6AD++LwF7roDxfp6Q+e\nNp1PcCfo8v6XOzQqazVf8/ul7S9p//H9Do2mA4r3yrck/b//J512mvncpk3SiBHS9u3OjAlA1CJ8\nAx3US9tfUsETBfrg4Aem8+kJ6frHxH/ozNPOdGhk1rqo10U667SzfMdew8uOl5HUEcL3mWdKb74p\nnXGG+fzOndJFFzEJE4AJ4RvoYLz1Xv303z/V1c9erWPVx0yv9c/orz+N+pO+MegbDo3OeoF2vHxy\n65NMvIyUjhC+pVMTSTdtki65xHz+yBGpqEj605+cGReAqBNU+F64cKH69eun1NRUFRQUaMOGDa1e\n/8EHH+jiiy9WWlqaevXqpZ///OeWDBZAeA5XHtZVz1yl+evn+712/dnXa3HhYvVOj/1e7+Ymn2Oe\neLn72G6t2bnGwRF1EBUV0oEDjccej9Snj3PjsVvnztIrr0jTppnP19ZKN98szZ4tGYYzYwMQNdoM\n38uWLdPMmTM1a9Ysbdu2TYWFhRo3bpz27t0b8Pry8nJdfvnlysvL05YtW/Twww/rgQce0IIFCywf\nPIDgvXvgXQ19YqhWfrHSdN7j8ujBKx7U09c+bdqSPZ50Tu2siWdNNJ1jx0ublJScqvL+z/9IvZv9\nINenj5SY6My4IiUpSfrDH6Rf/ML/tXnzpJtukqqrIz8uAFGjzfC9YMECTZs2TbfeeqsGDRqkRx55\nRHl5eVq0aFHA6//yl7+oqqpKS5cu1ZlnnqkJEyboRz/6EeEbcNDSbUtV+MdC/efof0znc9Nz9e+b\n/62ZI2bK1XS5tDjUfM3vF7e/qI8Pfay6+jqHRhQnvF7pjTekn/1MKiiQunc/VeV99tlTLRdNDRjg\nzBgjzeWSfvzjU9+D5GTza08/LV12mXnjIQAdSqt7R9fU1Gjr1q364Q9/aDpfVFSkTZs2BXzPG2+8\nodGjRyu5yR84RUVF+ulPf6rdu3erTzz/k2MLPn/9edVVnnB6GLYq2bVTkvRJ6acOj6RjMGTIW+9V\nneGVt75O3nqvvIY34Ll39r+j5dtfUPPpk+fkDNH9w+/XaaXpUuk7kqS0Tz459WIcBvFCI1n/X2U/\n7Tyy679nvJo86yy5JGWlZKtrahd1Se2irqld1eW////Ur67KTsmS2xW4VhHohxaXnP/+2fp70jCU\n8vkuZb26SZmvvamEo8fafo+kneO/qiP737F+PNFq9OlKX7ZQ/W+5V4mHjzae37BBVcMu0O77f6L6\njPQ2b8Ofr/EjVj9LT0qqBo6Z4PQw4kar4bu0tFRer1e5ubmm8zk5OSouLg74nuLiYvVu9k+NDe8v\nLi7ukOE7YeIkDThY6/QwbDXY6QGgRV+T9NOAr3wgzR9nOhMf65sE5pL0XIuvHv3vr52RGo7touX3\n5OEUadXp0h/Pl9Ye/In0+584PaSI+8pN0st/kc4oazyX8p+9GjRxelDvj5bPEuGL1c9yT9cEqTS+\nc0wktRq+QxHqP10fOxZc5SQWdfnskOL3qwOAlnl06gfArzk9EKfNE38PIGZlKb5zWqS12vPdrVs3\neTwelZSUmM6XlJQoLy8v4Hu6d+/uVxVveH/37t1N55OTk+XxeNo9aAAAAMBJHo/H1GYdrFbDd1JS\nkoYOHarVq1ebzq9Zs0aFhYUB33PRRRdp/fr1qm4ym3vNmjXq2bOnX8tJSkqKEhIsL74DAAAAtkpI\nSFBKSkrbFzbjMozWFx3961//qsmTJ2vhwoUqLCzU448/rsWLF+ujjz5Sfn6+7rvvPm3evFlr166V\ndGqpwUGDBmnMmDGaNWuWtm/frmnTpmnOnDm6++67Q/vqAAAAgDjQZtl54sSJKisr0/z583XgwAEN\nGTJEK1asUH5+vqRTkyh37mycpJSZmak1a9bou9/9rgoKCtSlSxd9//vfJ3gDAACgw2uz8g0AAADA\nGkFtL2+1b33rWzr99NOVlpamnJwcXXPNNfqkYX3h/zpy5IgmT56s7OxsZWdna8qUKcy0jTJHjhzR\n9773PQ0ePFhpaWnq3bu3vvOd7+jw4cN+1/FZRr8nnnhCY8eOVXZ2ttxut/bs2eN3DZ9l7Fi4cKH6\n9eun1NRUFRQUaMOGDU4PCa1Yt26drr76avXq1Utut1tLly71u2bOnDnq2bOn0tLSNHbsWH388ccO\njBRt+dWvfqVhw4YpKytLOTk5uvrqq/XRRx/5XcfnGf0ee+wxnXvuucrKylJWVpYKCwu1YsUK0zWh\nfI6OhO9hw4Zp6dKl+vTTT7Vq1SoZhqHLLrtMdXWNO83dcMMN2rZtm1atWqWVK1dq69atmjx5shPD\nRQv279+v/fv364EHHtCHH36oP//5z1q3bp3+//buL6Sp948D+PtstT+6P5i5ZihpYkZQMIwwLyQi\nRwuCIIICk7qxC4WlVwWWChElERUlZITUhZhS3ayCBYk1Nmr90UKpKCJ20VZB6TaUZHt+F+KhVfpL\n53fH8/2+XzDGnn0OfOC9sz2cHZ5n3759KXXMUh3Gx8exfft2tLW1zVjDLNXhxo0bOHz4MJqbmzE4\nOIjKykq4XC6EQiGlW6MZxONxbNiwAefPn4fRaPxt2d7Tp0/j7NmzuHjxIoLBIGw2G6qrqxGLxRTq\nmGYyMDCAhoYGBAIBPHjwAEuWLMG2bdvw7acdX5mnOhQWFqK9vR0vXrzAs2fPsHXrVuzatQtDQ0MA\n0shRLAJDQ0NCkiTx9u1bIYQQIyMjQpIk4ff75RqfzyckSRJv3rxRqk36C3fv3hUajUZEo1EhBLNU\no2AwKCRJEh8/fkwZZ5bqsWnTJlFXV5cyVlpaKo4ePapQRzQXJpNJXLt2TX6dTCaF3W4XJ0+elMfG\nx8eF2WwWly9fVqJFmoNYLCa0Wq3weDxCCOapdsuWLROdnZ1p5ajIle+fxeNxdHV1obS0FMXFxQCm\ntqg3mUzYvHmzXFdZWYns7GwEAgGlWqW/MDo6Cr1ej6ysLADM8t+EWarDjx8/8Pz5czidzpRxp9MJ\nv9+vUFeUjg8fPiASiaRkajAYUFVVxUxVYGxsDMlkEjk5OQCYp1olEgn09PRgYmICVVVVaeWo2OS7\no6MDZrMZZrMZHo8Hd+7ckdf8DofDyMvLS6mXJGnWbe1Jed+/f8exY8dQV1cHjWbqo8Us/z2YpTp8\n/foViUQCK1asSBlnTuo1nRszVSe32w2HwyFfuGCe6vLq1SuYTCYYDAbU1dWht7cXZWVlaeW4YJPv\n5uZmaDSaWR8PHz6U62tqajA4OIiBgQGsW7cOLpcL0Wh0odqhNMw1SwCIxWLYuXOnfH8ULQ7zyZKI\n1OPXe8NpcWlqaoLf78fNmzf/KivmufisXbsWL1++xJMnT9DQ0IC9e/fi6dOnsx7z/3JcsO0lGxsb\nUVtbO2vN9NrgwNR64BaLBSUlJaioqEBOTg5u376N2tpa2O12fPnyJeVYIQQ+f/782xb1tPDmmmUs\nFsOOHTug0Wjg8Xig0+nk95ilsuaa5WyYpTosX74cWq0WkUgkZTwSiSA/P1+hrigd0+dXJBJBQUGB\nPB6JRHjuLWKNjY3o7e1Ff38/ioqK5HHmqS5Lly7F6tWrAQAOhwPBYBCXLl3C8ePHAcwvxwWbfOfm\n5iI3N3dexyaTSQghkEgkAExtUR+LxRAIBOS/aQKBAOLx+Izb2tPCmUuW0WgULpcLkiTh3r178r3e\n05ilstI5L3/FLNVBp9OhvLwcXq8Xu3fvlsfv37+PPXv2KNgZzVdxcTHsdju8Xi/Ky8sBABMTE/D5\nfDhz5ozC3dGfuN1u9PX1ob+/H2vWrEl5j3mqWyKRQDKZTCtHbWtra2sGepW9f/8eV65cQVZWFiYn\nJzE8PAy3241Pnz7hwoULyM7ORl5eHh4/fozu7m44HA6EQiEcOnQIFRUVqK+vz2S7NItoNAqn04mx\nsTH09PQAmLoKHovFoNfrodVqmaWKhMNhvHv3Dq9fv8atW7dQXV2NeDwOvV4Po9HILFXEYrGgpaUF\nK1euhNFoxIkTJ+Dz+dDV1QWr1ap0e/QH8XgcIyMjCIfDuHr1KtavXw+r1YrJyUlYrVYkEgmcOnUK\nZWVlSCQSaGpqQiQSQWdnZ8q/jaS8+vp6XL9+HX19fSgoKJB/FyVJgk6ngyRJzFMljhw5AoPBgGQy\niVAohHPnzqG7uxvt7e0oKSmZf47/5HIsfxIKhYTL5RI2m03odDpRWFgoampqfluq7Nu3b6KmpkZY\nLBZhsVjE/v37xejoaKbbpVn09/cLSZKERqMRkiTJD41GIwYGBuQ6ZqkOLS0tKRlOP/+85BmzVI+O\njg5RVFQk9Hq92Lhxo3j06JHSLdEspr9Pf/1OPXjwoFzT2toq8vPzhcFgEFu2bBHDw8MKdkwz+dPv\noiRJoq2tLaWOeS5+Bw4cEKtWrRJ6vV7YbDZRXV0tvF5vSs18cuT28kREREREGaL4Ot9ERERERP8V\nnHwTEREREWUIJ99ERERERBnCyTcRERERUYZw8k1ERERElCGcfBMRERERZQgn30REREREGcLJNxER\nERFRhnDyTURERESUIf8D092v71cum8MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad38eccc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m6ZAQwq7IR+6qJDsb5w1rFE2xYeOpUaPkUzp2duKl038m5uezLPR7hnyUeZ419q61RE+F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\nE3MiIiIiIhfw/wHkt5ORVm9WZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xafbb528c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import division\n",
    "\n",
    "import scipy as sp\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = 0.1 # initial state\n",
    "Q = 1 # process noise covariance\n",
    "R = 1 # measurement noise covariance\n",
    "tf = 50 # simulation length\n",
    "\n",
    "N = 200 # number of particles in the particle filter\n",
    "\n",
    "xhat = x\n",
    "P = 2\n",
    "xhatPart = x\n",
    "\n",
    "# set the seed in order to duplicate the run exactly\n",
    "sp.random.seed(3)\n",
    "\n",
    "# Initialize the particle filter.\n",
    "xpart = x + math.sqrt(P)*sp.random.normal(size=N)\n",
    "\n",
    "xArr = [x] # true state\n",
    "yArr = [x**2/20 + math.sqrt(R)*sp.random.normal()] # observation\n",
    "xhatArr = [x] # state estimates of the Extended Kalman Filter\n",
    "PArr = [P]\n",
    "xhatPartArr = [xhatPart] # state estimates of the Particle Filter\n",
    "\n",
    "for k in range(tf):\n",
    "\n",
    "    ################################################\n",
    "    # System simulation\n",
    "    x = 0.5*x + 25*x/(1 + x*x) + 8*math.cos(1.2*k) + math.sqrt(Q)*sp.random.normal()\n",
    "    y = x**2/20 + math.sqrt(R)*sp.random.normal()\n",
    "\n",
    "    ################################################\n",
    "    # Extended Kalman filter\n",
    "    F = 0.5 + 25*(1 - xhat**2)/(1 + xhat**2)**2\n",
    "    #P = F*P*F.T + Q\n",
    "    P = F*P*F + Q\n",
    "    H = xhat/10\n",
    "    #K = P*H.T*(H*P*H.T + R)**(-1)\n",
    "    K = P*H*(H*P*H + R)**(-1)\n",
    "    xhat = 0.5*xhat + 25*xhat/(1 + xhat**2) + 8*math.cos(1.2*(k))\n",
    "    xhat = xhat + K*(y - xhat**2/20)\n",
    "    P = (1 - K*H)*P\n",
    "\n",
    "    ################################################\n",
    "    # Particle filter\n",
    "    xpartminus = 0.5*xpart + 25*xpart/(1 + xpart**2) + 8*math.cos(1.2*(k)) + math.sqrt(Q)*sp.random.normal(size=N)\n",
    "    ypart = xpartminus**2/20\n",
    "    q=(1/math.sqrt(R)/math.sqrt(2*math.pi))*sp.exp(-(y-ypart)**2/2/R)\n",
    "    #xpartminus = []\n",
    "    #q = []\n",
    "    #for i in range(N):\n",
    "    #    xpartminus.append(0.5*xpart[i] + 25*xpart[i]/(1 + xpart[i]**2) + 8*math.cos(1.2*(k)) + math.sqrt(Q)*sp.random.normal())\n",
    "    #    ypart = xpartminus[i]**2/20\n",
    "    #    vhat = y - ypart\n",
    "    #    q.append((1/math.sqrt(R)/math.sqrt(2*math.pi))*math.exp(-vhat**2/2/R))\n",
    "\n",
    "    # Normalize the likelihood of each a priori estimate.\n",
    "    qsum = sp.sum(q)\n",
    "    q = [i/qsum for i in q]\n",
    "    #for i in range(N):\n",
    "    #    q[i] = q[i]/qsum\n",
    "\n",
    "    # Resample.\n",
    "    for i in range(N):\n",
    "        u = sp.random.uniform() # uniform random number between 0 and 1\n",
    "        qtempsum = 0\n",
    "        for j in range(N):\n",
    "            qtempsum += q[j]\n",
    "            if qtempsum >= u:\n",
    "                xpart[i] = xpartminus[j]\n",
    "                break\n",
    "\n",
    "    # The particle filter estimate is the mean of the particles.\n",
    "    xhatPart = sp.mean(xpart)\n",
    "\n",
    "    ################################################\n",
    "    # Plot the estimated pdf's at a specific time.\n",
    "    if k == 33:\n",
    "        plt.figure()\n",
    "        plt.grid()\n",
    "        plt.title('Estimated pdf at k=%s'%k)\n",
    "        m = range(-25, 25)\n",
    "\n",
    "        ############################################\n",
    "        # Kalman filter pdf\n",
    "        pdf = (1/math.sqrt(P)/math.sqrt(2*math.pi)) * sp.exp(-(sp.array(m) - xhat)**2/2/P)\n",
    "        plt.plot(m, pdf, 'g', label='EKF')\n",
    "\n",
    "        ############################################\n",
    "        # Particle filter pdf\n",
    "        #pdf = sp.zeros((len(m),1))\n",
    "        #for i in xpart:\n",
    "        #    if m[0]<=i and i<=m[-1]:\n",
    "        #        pdf[math.ceil(i)-1+m[-1]] += 1\n",
    "        #    #for mm in m:\n",
    "        #    #    if (mm <= i) and (i < mm+1):\n",
    "        #    #        pdf[mm+m[-1]] = pdf[mm+m[-1]] + 1\n",
    "        #    #        break\n",
    "        #plt.plot(m, pdf/N, 'g', label='Particle filter')\n",
    "        #print 'min, max xpart[i] at k = 20: ', min(xpart), ', ', max(xpart)\n",
    "        pdf = sp.histogram(xpart, len(m), range=(m[0],m[-1]+1))#, new=True)\n",
    "        plt.plot(pdf[1][0:-1], pdf[0]/N, 'r', label='PF')\n",
    "\n",
    "        plt.legend()\n",
    "        plt.grid()\n",
    "        #plt.show()\n",
    "\n",
    "    # Save data\n",
    "    xArr.append(x) # true state\n",
    "    yArr.append(y) # observation\n",
    "    xhatArr.append(xhat) # state estimates of the Extended Kalman Filter\n",
    "    PArr.append(P)\n",
    "    xhatPartArr.append(xhatPart) # state estimates of the Particle Filter\n",
    "\n",
    "t = range(tf+1)\n",
    "plt.figure()\n",
    "#plt.grid()\n",
    "plt.title('State')\n",
    "plt.plot(t, xArr, 'b', label='True')\n",
    "plt.plot(t, xhatArr, 'g', label='EKF')\n",
    "plt.plot(t, xhatPartArr, 'r', label='PF')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "#plt.figure()\n",
    "#plt.plot(sp.sqrt(PArr))\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Why Should Engineers and Scientists Be Worried About Color?](http://www.research.ibm.com/people/l/lloydt/color/color.HTM)\n",
    "* [Matplotlib Gallery](http://matplotlib.org/gallery.html)\n",
    "* NJ Gordon, DJ Salmond, AFM Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation, *Radar and Signal Processing, IEE Proceedings F*, 1993\n",
    "* Liu, J.S., Chen, R. and Logvinenko, T. A theoretical framework for sequential importance sampling and resampling, in *Sequential Monte Carlo Methods in Practice*. Editors: A. Doucet, J.F.G. de Freitas and N. Gordon. Cambridge University Press. 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The version_information extension is already loaded. To reload it, use:\n",
      "  %reload_ext version_information\n"
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         "version": "Linux 3.13.0 51 generic i686 with Deepin 2014.3 trusty"
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       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 2.7.6 32bit [GCC 4.8.2] \\\\ \\hline\n",
       "IPython & 3.1.0 \\\\ \\hline\n",
       "OS & Linux 3.13.0 51 generic i686 with Deepin 2014.3 trusty \\\\ \\hline\n",
       "scipy & 0.13.3 \\\\ \\hline\n",
       "numpy & 1.8.2 \\\\ \\hline\n",
       "matplotlib & 1.3.1 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Thu Jun 04 10:08:00 2015 CST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 2.7.6 32bit [GCC 4.8.2]\n",
       "IPython 3.1.0\n",
       "OS Linux 3.13.0 51 generic i686 with Deepin 2014.3 trusty\n",
       "scipy 0.13.3\n",
       "numpy 1.8.2\n",
       "matplotlib 1.3.1\n",
       "Thu Jun 04 10:08:00 2015 CST"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext version_information\n",
    "%version_information scipy, numpy, matplotlib"
   ]
  }
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